Accenture

Advises businesses and runs their technology

AI Engineer - Associate Director

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeSingaporeNo salary listed

About the Job

Requirements

For the Consultant role, 3+ years of production software engineering experience in Python, data engineering, or analytics engineering is required.
For the Consultant role, hands-on experience building and deploying machine learning models in production environments is required.
For the Consultant role, understanding of supervised and unsupervised learning, feature engineering, and model evaluation is required.
For the Consultant role, experience with PyTorch, TensorFlow, scikit-learn, or XGBoost is required.
For the Consultant role, 2+ years of experience building data pipelines or ETL workflows is required.
For the Consultant role, strong SQL and data manipulation skills are required.
For the Consultant role, experience with model versioning, experiment tracking, and CI/CD for machine learning is required.
For the Consultant role, familiarity with AWS, Azure, or GCP and containerized workloads is required.
For the Manager role, 3+ years of experience building AI/ML systems in production, with operational accountability for deployed models, is required.
For the Manager role, experience facilitating client workshops to elicit requirements and design AI solutions is required.
For the Manager role, 2+ years of experience designing and delivering machine learning systems operating in production environments is required.
For the Manager role, experience developing value hypotheses and business cases for AI initiatives is required.
For the Manager role, hands-on experience with machine learning orchestration and MLOps, including model versioning, experiment tracking, and CI/CD, is required.
For the Manager role, experience across the machine learning development lifecycle, including problem definition, data preparation, model training, evaluation, serving, monitoring, and refinement, is required.
For the Manager role, experience with model evaluation frameworks, A/B testing, and production performance metrics is required.
For the Manager role, a track record of deploying production machine learning systems with measurable reliability, performance, or business outcomes is required.
For the Manager role, experience with guardrails, bias detection, fairness assessment, and compliance-focused audit trails is required.
For the Manager role, experience with advanced machine learning techniques, including deep learning, natural language processing, computer vision, or time-series forecasting, is required.
For the Manager role, 5+ years of experience in classical AI/ML, data engineering, or advanced analytics integrating intelligent systems into production software is required.
For the Manager role, 6+ years of full-stack engineering experience with Python and data engineering, with active hands-on capability across the stack, is required.
For the Manager role, 6+ years of cloud-native development on AWS, Azure, or GCP, including CI/CD, containerized workloads, infrastructure as code, and production operations, is required.
For the Manager role, 3+ years of technical leadership involving design ownership, code review, and engineer mentoring is required.
For the Associate Director role, 5+ years of delivering AI systems in production with verifiable evidence of enterprise-scale business value is required.
For the Associate Director role, experience as a recognized external AI thought leader, evidenced by published work, conference keynotes, or advisory board participation, is required.
For the Associate Director role, 5+ years of building AI/ML applications and current understanding of the full AI technical stack, including data infrastructure, feature engineering, model training, serving, monitoring, and MLOps, is required.
For the Associate Director role, experience building or significantly scaling an AI engineering practice, including defining capability strategy, hiring at scale, and developing its intellectual property and standards, is required.
For the Associate Director role, hands-on technical depth to set architecture standards, govern engineering quality across a portfolio, and direct senior engineers and architects on consequential technical decisions is required.
For the Associate Director role, experience with strategic vendor and partner relationships involving hyperscalers, machine learning platforms, or AI framework companies in enterprise delivery is required.
For the Associate Director role, a track record of independent C-suite relationship management, influencing strategic technology decisions, and building board-level trust is required.
For the Associate Director role, experience in at least two regulated industries, such as financial services, healthcare, telecommunications, or energy, where compliance, data residency, and AI governance affect architecture, is required.
For the Associate Director role, experience leading enterprise AI transformation programmes, including value hypotheses, business cases, process reengineering, and outcome measurement at scale, is required.
For the Associate Director role, experience delivering global multi-country, multi-entity, and multi-language AI programmes with complex governance is required.
For the Associate Director role, 12+ years of experience in classical AI/ML, data engineering, and advanced analytics, delivering intelligent systems at scale in complex regulated enterprise environments, is required.
For the Associate Director role, deep familiarity with enterprise AI economics, including total cost of ownership modelling, build-versus-buy decisions, make-versus-partner strategies, and board-level AI investment governance, is required.
For the Associate Director role, 14+ years of full-stack engineering and software delivery, with sustained technical depth and current engagement in AI and broader technology developments, is required.
For the Associate Director role, 10+ years of cloud-native development and deep understanding of enterprise cloud architecture, infrastructure, DevOps, and production operations at scale is required.
For the Associate Director role, ability to attract, hire, and retain exceptional AI engineering talent is required.
For the Associate Director role, expert-level experience in digital transformation and complex enterprise software delivery in large, regulated, or multinational environments is required.
For the Associate Director role, 10+ years of technical and delivery leadership, including portfolio management, commercial accountability, and senior-level team development, is required.
For the Associate Director role, a track record of sales and account growth, including winning new business, growing existing accounts, and contributing to revenue targets, is required.
For the Associate Director role, experience hiring and developing senior engineering talent in competitive markets is required.

Responsibilities

For the Consultant role, design and build production-standard AI applications with Python backends, REST and event-driven APIs, and frontend interfaces.
For the Consultant role, implement AI application workflows including model serving, prediction pipelines, real-time inference, and integrations with business systems.
For the Consultant role, translate business requirements into technical specifications, clarify scope and ambiguities with managers and clients, and deliver agreed outcomes.
For the Consultant role, build, train, and deploy machine learning models using established frameworks, including feature engineering, model selection, hyperparameter tuning, and lifecycle management.
For the Consultant role, apply supervised learning, unsupervised learning, time-series forecasting, classification, and regression techniques to appropriate use cases.
For the Consultant role, design and maintain model versioning, experiment tracking, and reproducible training pipelines using MLOps tools.
For the Consultant role, design and implement data pipelines for ingestion, transformation, enrichment, and preparation at scale.
For the Consultant role, build ETL/ELT workflows connecting data sources to machine learning training and inference systems.
For the Consultant role, implement data quality checks, validation, and monitoring to maintain data integrity throughout pipelines.
For the Consultant role, integrate trained models into production systems with error handling, monitoring, and rollback capabilities.
For the Consultant role, design and implement batch prediction, real-time API, and, where required, edge-deployment model-serving architectures.
For the Consultant role, build machine learning monitoring and observability, including prediction drift detection, model performance tracking, and automated retraining triggers.
For the Consultant role, maintain CI/CD pipelines for model code, training scripts, and infrastructure, including automated testing and deployment strategies.
For the Consultant role, instrument machine learning systems for production observability, including latency tracking, prediction profiling, model performance logging, and anomaly detection.
For the Consultant role, build model test suites with unit tests, integration tests, held-out dataset validation, data drift detection, and model decay alerts.
For the Consultant role, maintain current knowledge of machine learning frameworks, tools, and research, test new approaches, and bring relevant innovations into team engineering practice.
For the Consultant role, document patterns, build reusable components, and support capability development through internal knowledge sharing.
For the Manager role, architect and deliver end-to-end production AI solutions, contributing directly to code and design when needed.
For the Manager role, design model harnesses, training pipelines, serving architectures, and integration patterns for diverse use cases.
For the Manager role, build and maintain data pipelines, implement advanced knowledge retrieval components, and design model lifecycle management.
For the Manager role, establish machine learning architecture standards for model versioning, experimentation, evaluation, and feature engineering.
For the Manager role, lead the design and implementation of MLOps practices, including CI/CD for machine learning, automated testing, model registry management, and deployment strategies.
For the Manager role, build production AI systems hands-on, resolve complex engineering problems, and set quality standards through personal example.
For the Manager role, design and implement end-to-end machine learning pipelines covering data preparation, feature engineering, model training, evaluation, serving, and monitoring.
For the Manager role, build and operate evaluation and MLOps pipelines using benchmark datasets, performance metrics, A/B testing, machine learning test suites, and production observability.
For the Manager role, implement model explainability, bias detection and mitigation, data privacy safeguards, and audit trails for enterprise compliance.
For the Manager role, serve as the primary technical contact for client stakeholders, facilitate workshops, translate requirements into AI solution designs, and communicate technical decisions to non-technical audiences.
For the Manager role, develop initial value hypotheses by identifying AI automation and augmentation opportunities, estimating business impact, and establishing baseline metrics.
For the Manager role, contribute to solution design and proposal development, identify engagement expansion opportunities, and support account growth.
For the Manager role, lead agile workstream delivery by managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility.
For the Manager role, establish MLOps and DevOps practices for model code and training scripts, evaluation gates, deployment, model and asset registries, and production operations.
For the Manager role, define and implement evaluation frameworks, model test suites, human-in-the-loop feedback capture, and production observability for performance, cost, latency, and drift.
For the Manager role, implement guardrails, fairness checks, bias mitigation, and audit trails for responsible AI and enterprise compliance.
For the Manager role, lead, manage, and develop Consultants and Analysts by setting expectations, providing technical coaching, and running code reviews and design sessions.
For the Manager role, foster engineering rigor, continuous improvement, and learning, and support team members in developing AI expertise through challenging work and knowledge sharing.
For the Manager role, maintain hands-on knowledge of AI developments, test frameworks, tools, and research, and bring relevant advances into team practice.
For the Manager role, contribute reusable accelerators, reference implementations, and delivery standards to internal practice development.
For the Associate Director role, lead executive AI transformation strategy discussions with CXOs and board-level sponsors, build advisory relationships, and influence strategic technology investment decisions.
For the Associate Director role, diagnose business problems through strategic workshops and process analysis, identify AI transformation opportunities, and design end-to-end solutions addressing root causes.
For the Associate Director role, develop and own enterprise-level value hypotheses and business cases, baseline performance, and commit to measurable outcomes tracked through and after delivery.
For the Associate Director role, define client AI transformation roadmaps, prioritize initiatives by strategic value and readiness, manage board-level change, and support sustainable AI adoption.
For the Associate Director role, set architecture principles and technology choices across enterprise AI programmes while maintaining hands-on depth to evaluate trade-offs and direct senior engineers.
For the Associate Director role, govern portfolio-wide technical standards for machine learning development, data pipelines, model serving, evaluation, and production readiness.
For the Associate Director role, stay current with AI developments, evaluate frameworks, models, and tools for enterprise production, and ensure delivery practices reflect relevant advances.
For the Associate Director role, lead concurrent AI programme portfolios, monitor scope, quality, risk, and commercial performance, and intervene when escalation or correction is needed.
For the Associate Director role, own programme-level commercial accountability for contracted milestones, margin management, change control, and senior-level client satisfaction.
For the Associate Director role, establish and enforce portfolio delivery standards covering engineering rigor, MLOps, value measurement, and client communication.
For the Associate Director role, grow client relationships from project delivery into strategic partnerships through demonstrated impact and senior trust.
For the Associate Director role, lead pursuit efforts by shaping win strategies, directing solution design, authoring and presenting proposals, and negotiating commercially at senior levels.
For the Associate Director role, identify AI market opportunities and mobilize the practice to pursue high-value applications in industries and functions.
For the Associate Director role, build, hire, and develop senior managers, managers, and engineers; define role profiles, lead senior hiring decisions, manage performance, and foster retention and growth.
For the Associate Director role, develop the AI practice by defining capability strategy, building intellectual property and reusable accelerators, and establishing practice standards.
For the Associate Director role, champion AI adoption within Accenture and with clients, and communicate a credible vision for enterprise AI.
For the Associate Director role, shape Accenture’s perspective on AI through thought leadership, industry events, research-community engagement, and practice representation.
For the Associate Director role, champion continuous learning and innovation and bring AI research, tools, and engineering developments into client delivery.
For the Associate Director role, identify strategic partnerships with platform vendors, hyperscalers, and AI framework providers to strengthen capability and market position.

Desired Qualifications

For the Consultant role, experience with deep learning, natural language processing, computer vision, or time-series forecasting is a strong signal.
For the Consultant role, production experience with BentoML, KServe, Seldon, or FastAPI for model serving is a strong signal.
For the Consultant role, exposure to MLflow, Weights & Biases, or equivalent experiment-tracking tools is a strong signal.
For the Consultant role, experience with data quality and validation frameworks is a strong signal.
For the Consultant role, hands-on software engineering experience with version control, code review, testing, and CI/CD is a strong signal.
For the Consultant role, cloud-native development experience with Kubernetes, managed services, and infrastructure as code is a strong signal.
For the Consultant role, experience translating business requirements into machine learning solutions is a strong signal.
For the Consultant role, active AI/ML community engagement through open-source contributions, side projects, or published learning is a strong signal.
For the Consultant role, a bachelor's degree in Computer Science, Engineering, or a related field is a strong signal.
For the Consultant role, proficiency with Claude Code or GitHub Copilot is a strong signal.
For the Manager role, experience integrating AI systems with enterprise platforms such as SAP, Salesforce, or ServiceNow through APIs is a strong signal.
For the Manager role, experience on complex, enterprise-scale, multi-workstream, client-facing digital transformation programmes is a strong signal.
For the Manager role, contributions to internal accelerators, reusable frameworks, or AI thought leadership are a strong signal.
For the Manager role, experience engaging directly with C-suite stakeholders and translating business requirements into technical solutions is a strong signal.
For the Manager role, a bachelor's degree in a related field is listed as preferred, and a master's degree is highly valued.
For the Associate Director role, experience with deep learning, natural language processing, computer vision, generative AI, or reinforcement learning at enterprise scale is a strong signal.
For the Associate Director role, a background in data science, analytics, or AI research before engineering leadership is a strong signal.
For the Associate Director role, experience with responsible AI, including fairness assessment, bias mitigation, explainability, and governance frameworks, is a strong signal.
For the Associate Director role, a bachelor's degree in a related field is listed as preferred; a master's degree in Computer Science, AI, or Engineering, or an MBA, is highly valued.

AI ENGINEER – CONSULTANT

AI Engineer – Consultant
Data & AI | Accenture

About Accenture Data & AI

The beginning of a new Data & AI decade that will reshape work and society is underway. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimise and reinvent their businesses with data and AI — backed by a $3 billion investment and a commitment to industry-defining work.

With over 45,000 professionals dedicated to Data & AI, Accenture’s Data & AI organisation brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the frontier of what is possible.

About the Role

The AI Engineer (Consultant) is a hands-on engineer at the core of Accenture’s AI delivery capability. This role is for those who build intelligent systems — machine learning pipelines, AI-powered applications, data-driven solutions, and AI integrations — and who want to do that work at the frontier of enterprise AI. Consultants work within delivery teams, turning business requirements into production-grade AI solutions under the guidance of technical leads and managers.

The expectation is active, daily engineering: writing code, building machine learning models, designing AI pipelines, implementing integrations, and maintaining the MLOps and DevOps practices that keep production systems running. Consultants on this team are also expected to invest continuously in learning — AI is evolving rapidly, and staying current is a professional responsibility, not optional.

Position Responsibilities

AI Application Development

Design and build AI-powered applications — Python backends, REST and event-driven APIs, and frontend interfaces — to production engineering standards
Implement AI application workflows: model serving, prediction pipelines, real-time inference, and integration with business systems
Translate business requirements into technical specifications; work with managers and clients to clarify scope, surface ambiguities, and deliver against agreed outcomes

Machine Learning Development

Build, train, and deploy machine learning models using established frameworks (PyTorch, TensorFlow, scikit-learn, LightGBM): feature engineering, model selection, hyperparameter tuning, and lifecycle management
Implement appropriate ML techniques for different use cases: supervised learning, unsupervised learning, time series forecasting, classification, and regression
Design and maintain model versioning, experimentation tracking, and reproducible training pipelines using MLOps tools

Data Engineering & Pipelines

Design and implement data pipelines: ingestion, transformation, enrichment, and preparation at scale
Build ETL/ELT workflows connecting data sources to ML training and inference systems
Implement data quality checks, validation, and monitoring to ensure data integrity throughout the pipeline

Model & System Integration

Integrate trained models into production systems with robust error handling, monitoring, and rollback capabilities
Design and implement model serving architectures: batch prediction, real-time APIs, and edge deployment where required
Build ML monitoring and observability: prediction drift detection, model performance tracking, and automated retraining triggers

MLOps & Production Excellence

Maintain CI/CD pipelines for model code, training scripts, and infrastructure — including automated testing and deployment strategies
Instrument ML systems with production observability: latency tracking, prediction profiling, model performance logging, and anomaly detection
Build model testing suites: unit tests, integration tests, and validation against held-out datasets; implement data drift detection and model decay alerting

Continuous Learning

Maintain active, current knowledge of ML frameworks, tooling, and research — testing new approaches and bringing relevant innovations into the team’s engineering practice
Contribute to internal knowledge sharing: documenting patterns, building reusable components, and supporting capability development within the team

Travel may be required for this role. The extent of travel will vary from 0% to 100% depending on client requirements and business need.

Why This Role

This team builds some of the most technically advanced AI systems in enterprise technology today. For an engineer at the start of their career in AI, there is no better environment to learn fast, work on hard problems, and build the depth and breadth that will define the next decade of enterprise AI. The work ships. The feedback loop is tight. The team is exceptional.

Accenture is an Equal Opportunity Employer. All employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, disability, or any other basis protected by applicable law.

Core Pillars: Hands-On Builder | ML Engineering | Production Excellence | Continuous Learner

Core Requirements

3+ years of software engineering in production — Python, data engineering, or analytics engineering
Hands-on experience building and deploying machine learning models in production environments
Strong understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation
Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn, or XGBoost)
2+ years building data pipelines or ETL workflows
Strong SQL and data manipulation skills
Experience with MLOps practices: model versioning, experiment tracking, CI/CD for ML
Familiarity with cloud platforms (AWS, Azure, or GCP) and containerised workloads

Additional Strong Signals

Experience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting
Production experience with model serving frameworks (BentoML, KServe, Seldon, or FastAPI)
Exposure to experiment tracking tools (MLflow, Weights & Biases, or equivalent)
Experience with data quality and validation frameworks
Hands-on experience with classical software engineering: version control, code review, testing, and CI/CD
Cloud-native development: Kubernetes, managed services, infrastructure as code
Experience translating business requirements into ML solutions
Active engagement with the AI/ML community: open source contributions, side projects, or published learning
Bachelor’s degree in Computer Science, Engineering, or a related field
Proficiency with AI-assisted development tools (Claude Code, GitHub Copilot)

AI ENGINEER – MANAGER

AI Engineer – Manager
Data & AI | Accenture

About Accenture Data & AI

About the Role

The AI Engineer (Manager) leads the technical delivery of AI programmes while remaining an active, hands-on engineer. This role bridges the gap between business problems and technical solutions — working directly with clients to understand requirements, translating them into AI solution designs, and leading a team of engineers to deliver production-grade systems that generate measurable value.

Managers on this team operate as Forward Deployed Engineers — brought into client environments to rapidly understand a business problem, design an AI solution, and build it. The expectation is genuine technical depth combined with client credibility: the Manager must be as effective in a code review or design session as they are in a client workshop. They develop their team, grow client relationships, and continuously evolve their knowledge of AI as the field advances.

Position Responsibilities

Technical Solution Design and Hands-On Delivery

Architect and deliver AI solutions end-to-end — machine learning systems, data pipelines, model serving, and AI-powered applications — at production engineering standards, contributing directly to code and design when required
Design model harnesses, training pipelines, serving architectures, and integration patterns for diverse use cases
Build and maintain data pipelines, implement advanced knowledge retrieval components, and design robust model lifecycle management
Establish ML architecture standards — model versioning, experimentation methodology, evaluation frameworks, and feature engineering patterns
Lead the design and implementation of MLOps practices: CI/CD for ML, automated testing, model registry management, and deployment strategies

AI Technical Delivery

Architect and build production AI systems hands-on — model training, serving, monitoring, and integration; write code, resolve complex engineering problems, and set the quality standard through personal example
Design and implement end-to-end ML pipelines: data preparation, feature engineering, model training, evaluation, serving, and continuous monitoring
Build and operate evaluation and MLOps pipelines: benchmark datasets, model performance metrics, A/B testing frameworks, ML testing suites (unit, integration, simulation), and production observability
Implement trust, safety, and governance components: model explainability, bias detection and mitigation, data privacy safeguards, and audit trail design for enterprise compliance

Client Engagement and Business Translation

Serve as the primary technical point of contact for client stakeholders — running workshops, translating business requirements into AI solution designs, and communicating technical decisions clearly to non-technical audiences
Develop initial value hypotheses for AI solutions — identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins
Contribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growth

Delivery Excellence and MLOps

Lead workstream delivery in agile environments — managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility
Establish MLOps and DevOps practices: CI/CD for model code and training scripts, automated evaluation gates, deployment strategies, model and asset registry management, and production operations
Define and implement evaluation frameworks, model testing suites, HITL feedback capture, and production observability — performance tracking, cost monitoring, latency profiling, and drift detection
Implement guardrails, fairness checks, bias mitigation, and audit trail design for enterprise compliance and responsible AI

Team Leadership and Development

Lead, manage, and develop a team of Consultants and Analysts — setting clear expectations, providing technical coaching, and running structured code reviews and design sessions
Foster a delivery culture of engineering rigour, continuous improvement, and learning — supporting team members in building AI depth through challenging work and active knowledge sharing

Innovation and Continuous Learning

Maintain current, hands-on knowledge of AI developments — testing new frameworks, tooling, and research; bringing relevant advances into the team’s engineering practice
Contribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture’s AI practice

Travel may be required for this role. The extent of travel will vary from 0% to 100% depending on client requirements and business need.

Why This Role

This role is for engineers ready to lead — technically and with clients. You will own the full arc of AI delivery: from understanding the business problem to shipping production systems that demonstrably move the needle. The team operates at the frontier, the clients are serious, and the work has real consequences. If you are technically excellent, client-credible, and driven to build things that matter, this is the right next step.

Core Pillars: Hands-On Technical Lead | AI Delivery | Client Engagement | Team Leadership

Core Requirements

3+ years building AI/ML systems in production — with operational accountability for deployed models
Experience facilitating client workshops to elicit requirements and design AI solutions
2+ years designing and delivering ML systems — models operating with meaningful value delivery in real production environments
Experience developing value hypotheses and business cases for AI initiatives
Hands-on experience with ML orchestration and MLOps (model versioning, experiment tracking, CI/CD for ML)
Demonstrated experience across the ML Development Lifecycle: problem definition, data preparation, model training, evaluation, serving, monitoring, and refinement
Experience with model evaluation frameworks, A/B testing, and performance metrics in production
Proven track record deploying ML systems in production with measurable results — reliability, performance, or business value outcomes
Experience with guardrails, bias detection, fairness assessment, and compliance-focused audit trails
Experience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting
5+ years experience in classical AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software
6+ years full stack engineering: Python and data engineering; active, hands-on capability across the stack
6+ years cloud-native development on AWS, Azure, or GCP — CI/CD, containerised workloads, infrastructure as code, and production operations
3+ years technical leadership: design ownership, code review, and engineer mentoring

Additional Strong Signals

Experience integrating AI systems with enterprise platforms (SAP, Salesforce, ServiceNow) via APIs
Experience on complex digital transformation programmes — enterprise-scale, multi-workstream, client-facing delivery
Contributions to internal accelerators, reusable frameworks, or thought leadership in AI
Experience engaging directly with C-suite stakeholders — translating business requirements to technical solutions
Bachelor’s degree in a related field. A Master’s degree is highly valued.

AI ENGINEER – ASSOCIATE DIRECTOR

AI Engineer – Associate Director
Data & AI | Accenture

About Accenture Data & AI

About the Role

The AI Engineer – Associate Director is a senior leader who combines deep AI engineering credibility with the strategic and commercial acumen to drive enterprise-scale transformation. This role operates at the highest level of client engagement — working independently with CXOs and board-level sponsors to define the AI transformation agenda, build the business case, and lead the programmes that deliver it.

Associate Directors are Forward Deployed Engineers at the most senior level — able to engage a CEO or CTO, diagnose the business problem, and design and direct the build of an AI solution that solves it. They remain personally current with developments in AI, maintaining the technical depth to govern architecture standards, direct senior engineers credibly, and evaluate the most consequential technology trade-offs. They are simultaneously portfolio leaders, practice builders, and commercial drivers.

This is not a role for those who have left engineering behind. The expectation is a leader who is technically credible, commercially accountable, client-trusted at the most senior level, and driven to build and develop the next generation of AI engineers and the practice they work in.

Position Responsibilities

CXO Engagement and Business Transformation Strategy

Engage independently at CXO and board level — leading executive conversations on AI transformation strategy, building trusted advisory relationships, and influencing strategic technology investment decisions
Diagnose complex business problems as a forward-deployed engineer: facilitating strategic workshops, synthesising business process analysis into AI transformation opportunities, and designing end-to-end AI solutions that address root causes, not symptoms
Develop and own enterprise-level value hypotheses: constructing rigorous business cases with financial modelling, baselining current-state performance, and committing to measurable outcomes that can be tracked through delivery and post-deployment
Define the client’s AI transformation roadmap — prioritising initiatives by strategic value and execution readiness, managing board-level change, and ensuring sustainable adoption of AI across the organisation

Enterprise Solution Architecture and Technical Direction

Set architecture principles and make consequential technology choices for enterprise AI programmes — spanning model selection, data infrastructure, serving architectures, MLOps discipline, and governance; maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and direct senior engineers credibly
Govern technical standards across the portfolio: ML development patterns, data pipeline architecture, model serving strategies, evaluation frameworks (benchmark datasets, performance metrics, A/B testing), and production readiness criteria — ensuring engineering disciplines are institutionalised, not improvised
Stay personally current with AI developments — evaluating new frameworks, models, and tooling; determining what is ready for enterprise production; and ensuring the practice’s delivery approach reflects the leading edge of what is possible

Portfolio Governance and Delivery Excellence

Lead a portfolio of concurrent AI programmes — maintaining visibility across scope, quality, risk, and commercial performance; intervening decisively when escalation or course correction is required
Own programme-level commercial accountability: delivery against contracted milestones, margin management, change control, and client satisfaction at the highest level
Establish and enforce delivery standards across the portfolio — from engineering rigour and MLOps discipline to value measurement and client communication — that define what excellent delivery looks like

Sales Growth and Business Development

Own and grow a portfolio of client relationships — developing accounts from project delivery into multi-year strategic partnerships through demonstrable impact and senior trust
Lead pursuit efforts for significant new opportunities: shaping win strategies, directing solution design, authoring and presenting proposals, and engaging in commercial negotiations at the most senior level
Identify and develop new market opportunities for Accenture’s AI capability — spotting where the technology is ready to create step-change value in specific industries or functions and mobilising the practice to pursue them

Practice Building, Hiring, and Leadership

Build, hire, and develop a high-performing team of Senior Managers, Managers, and engineers — defining role profiles, leading senior hiring decisions, managing performance, and creating an environment where exceptional people choose to stay and grow
Drive the development of Accenture’s AI practice: defining the capability strategy, building the IP — reference architectures, delivery frameworks, value engineering tools, and reusable accelerators — and establishing the standards that the practice operates to
Act as a change leader: championing the adoption of AI both within Accenture and with clients; inspiring teams, clients, and the market with a clear and credible vision of where enterprise AI is going and how to get there

Innovation Leadership and Market Positioning

Shape Accenture’s point of view on AI: contributing to published thought leadership, representing the practice at industry events, engaging with the research community, and building Accenture’s reputation as the leading enterprise AI partner
Champion continuous learning and innovation across the practice — ensuring that new developments in AI research, tooling, and engineering practice flow rapidly from the frontier into client delivery
Identify strategic partnerships with platform vendors, hyperscalers, and AI framework providers that strengthen Accenture’s capability and competitive position in the market

This role is for those who have already demonstrated that they can lead at the highest level — technically, commercially, and with clients — and who want to do it in the most consequential domain in enterprise technology today. You will define what enterprise AI looks like, build the team and practice that delivers it, and create measurable transformation for clients at the most senior level. If you have the technical credibility, the client trust, and the drive to build something that matters at scale, this is where that work happens.
Core Requirements

5+ years delivering AI systems in production — with verifiable evidence of enterprise-scale business value created
Experience operating as a recognised external thought leader in AI — published work, conference keynotes, or advisory board participation
5+ years building AI/ML applications — deep, current understanding of the full AI technical stack: data infrastructure, feature engineering, model training, serving, monitoring, and MLOps
Experience building or significantly scaling an AI engineering practice — defining capability strategy, hiring at scale, and developing the IP and standards the practice runs on
Hands-on technical depth sufficient to set architecture standards, govern engineering quality across a portfolio, and direct senior engineers and architects credibly on consequential technical decisions
Experience with strategic vendor and partner relationships — hyperscalers, ML platforms, AI framework companies — in the context of enterprise delivery
Demonstrated track record of CXO-level client engagement — independently managing C-suite relationships, influencing strategic technology decisions, and building board-level trust
Experience in two or more regulated industries (financial services, healthcare, telecoms, energy) where compliance, data residency, and AI governance shape architecture at the deepest level
Proven experience leading enterprise AI transformation programmes — value hypothesis development, business case construction, process reengineering, and outcome measurement at scale
Experience with global programme delivery — multi-country, multi-entity, multi-language AI programmes with complex governance structures
12+ years experience in classical AI/ML, data engineering, and advanced analytics — with a track record of delivering intelligent systems at scale in complex, regulated enterprise environments
Deep familiarity with enterprise AI economics: total cost of ownership modelling, build vs buy decisions, make vs partner strategies, and AI investment governance at board level
14+ years of full stack engineering and software delivery: sustained technical depth with active, current engagement in AI and broader technology developments
10+ years cloud-native development — deep understanding of enterprise cloud architecture, infrastructure, DevOps, and production operations at scale
A proven ability to attract, hire, and retain exceptional AI engineering talent — recognised by candidates as someone they want to work for
Expert-level experience in digital transformation and complex enterprise software delivery across large, regulated, or multi-national environments
10+ years of technical and delivery leadership — portfolio management, commercial accountability, and team development at senior levels
Proven track record of sales and account growth: pursuing and winning significant new business, growing existing accounts, and contributing to revenue targets
Experience hiring and developing senior engineering talent in competitive markets

Additional Strong Signals

Experience with advanced ML techniques: deep learning, NLP, computer vision, generative AI, or reinforcement learning at enterprise scale
Background in data science, analytics, or AI research prior to engineering leadership
Experience with responsible AI: fairness assessment, bias mitigation, explainability, and governance frameworks
Bachelor’s degree in a related field. A Master’s degree in Computer Science, AI, Engineering, or an MBA is highly valued.

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us atwww.accenture.com

Equal Employment Opportunity Statement

We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size: 10,001+
Company stage: IPO
Headquarters: Dublin, Ireland
Founded: 1989

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