Agile – Senior Manager - MS, Fdl, AI Architect

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Job Title: Senior Manager – AI Architect

Summary:

We are looking for a highly skilled and experienced Senior Manager – AI Architect to lead the strategy, architecture, and governance of enterprise AI solutions supporting data products and corporate finance transformation.

This role combines AI/ML architecture, technical leadership, and people management, translating business needs into secure, scalable, and responsible AI solutions. The ideal candidate will integrate predictive machine learning, generative AI, and appropriately controlled agentic workflows with enterprise applications and data to enable transformation across P2P, O2C, R2R, and FP&A.

You will own AI architecture quality and technology roadmaps across engagements, lead architects and technical specialists, and partner with data science, engineering, product, security, risk, and business teams to guide solutions from discovery through production adoption and measurable value realization.

Responsibilities:

AI Strategy & Architecture Leadership

• Define the AI architecture strategy and roadmap, aligning prioritized use cases with enterprise technology standards, data readiness, risk appetite, and business objectives.
• Lead discovery and feasibility assessments; determine when rules, analytics, traditional ML, or generative AI are appropriate, and validate options through measurable proofs of concept.
• Design end-to-end AI architectures spanning data ingestion, feature or embedding pipelines, model training or adaptation, retrieval, inference, orchestration, APIs, and enterprise integration.
• Evaluate model providers, open-weight models, cloud AI services, and build-versus-buy options; balance quality, latency, scalability, cost, licensing, data residency, and vendor dependency.
• Establish reference architectures and design standards for retrieval-augmented generation (RAG), model serving, and agentic workflows, including permission-aware retrieval, tool authorization, and human approval for consequential actions.
• Lead architecture reviews and resolve complex AI design challenges; guide model selection, prompting, fine-tuning where justified, evaluation design, and integration with production systems.

AI Delivery, Governance & Operational Excellence

• Provide architecture oversight across AI initiatives, defining acceptance criteria, technical dependencies, delivery risks, and stage gates from experimentation through production readiness.
• Partner with engineering and platform teams to establish MLOps and LLMOps practices, including versioned datasets, models and prompts, reproducible evaluation, deployment controls, and rollback.
• Define task-specific evaluation frameworks covering predictive performance, groundedness, retrieval quality, hallucination risk, robustness, fairness where relevant, latency, and cost; validate against representative test data and business baselines.
• Design production monitoring for data and model drift, output quality, reliability, usage, and inference spend; establish fallback behavior, incident response, and controlled model or provider updates.
• Embed responsible AI, privacy, security, and applicable compliance controls with risk and legal teams, including data provenance, audit trails, prompt-injection defenses, sensitive-data protection, and risk-based human oversight.

Team Leadership & Talent Management

• Lead and scale an AI architecture team, coordinating data scientists, ML engineers, data engineers, and application specialists across engagements; develop architects and technical leads.
• Build an inclusive, high-performance AI practice focused on rigorous experimentation, responsible innovation, reusable knowledge, and measurable business outcomes.
• Own hiring, resource planning, performance management, succession planning, and capability development aligned with AI delivery demand and evolving technology needs.
• Mentor architects and technical specialists on AI design, evaluation rigor, production readiness, stakeholder influence, and career growth; strengthen peer reviews and communities of practice.

Business & Stakeholder Management

• Partner with finance transformation leaders, product owners, and clients to identify AI opportunities, clarify process constraints, and define measurable success criteria.
• Present AI solution options, limitations, investment needs, and risk trade-offs to decision-makers; align expectations on automation boundaries, human review, and business value.
• Lead AI architecture inputs for proposals, RFP responses, and demonstrations, including solution narratives, prototypes, estimates, data requirements, and implementation assumptions.
• Communicate architecture decisions, evaluation results, residual risks, adoption readiness, and realized business impact clearly to executive and technical audiences.

Reusable AI Solutions & Innovation

• Develop reusable AI reference designs, evaluation suites, retrieval components, model integration patterns, and governance templates to accelerate consistent delivery.
• Assess emerging AI capabilities through structured experiments and business cases; scale only where evidence supports performance, operational feasibility, and acceptable risk.
• Enable finance-focused capabilities such as document intelligence, knowledge assistants, forecasting, and anomaly detection, with traceable outputs and controls appropriate to each use case.

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI, or a related field, or equivalent practical experience.
• 12+ years of experience across software engineering, data platforms, solution architecture, or AI/ML, including substantial experience architecting and deploying production AI/ML solutions.
• 5+ years of technical people-management experience, with a proven track record of leading architects or engineering teams and delivering complex, cross-functional AI or data initiatives.

Technical Expertise

• Deep experience designing AI solutions on at least one major cloud platform (Azure, AWS, or GCP), including managed AI services, deployment options, security, resilience, and cost optimization.
• Strong foundations in machine learning, model evaluation, and applied statistics, with practical expertise in generative AI, LLMs, embeddings, vector search, RAG, and model adaptation trade-offs.
• Strong Python and software architecture skills, with experience in ML frameworks (e.g., PyTorch, TensorFlow, or scikit-learn), APIs, data pipelines, and enterprise application integration.
• Experience with MLOps and LLMOps, including experiment tracking, model registries, evaluation automation, CI/CD, model serving, observability, and lifecycle management.
• Strong understanding of AI security and responsible AI, including data leakage, prompt injection, model limitations, explainability, fairness assessment, access controls, and human-in-the-loop design.

AI Architecture & Delivery Skills

• Proven ability to guide AI solutions from use-case discovery and feasibility assessment through architecture, evaluation, production deployment, and operational transition.
• Experience conducting architecture reviews, model and vendor evaluations, AI risk assessments, and production-readiness reviews across multidisciplinary teams.
• Strong skills in technical estimation, capacity planning, inference cost modeling, and total cost of ownership analysis, balancing model quality, latency, reliability, and business value.

Domain & Functional Knowledge

• Understanding of enterprise data and business processes; exposure to corporate finance processes (P2P, O2C, R2R, and FP&A) and associated control requirements is preferred.
• Experience applying AI/ML to finance transformation, ERP-connected workflows, document processing, forecasting, or enterprise analytics is highly desirable.

Leadership & Behavioral Skills

• Strong people leadership, practice development, and coaching skills, with the ability to lead architects and influence data science, engineering, and business teams.
• Excellent stakeholder management and communication skills, including executive-level engagement and the ability to explain AI capabilities, limitations, and risks without overstating results.
• Ability to balance AI strategy with pragmatic implementation, challenge assumptions using evidence, and make sound decisions amid rapidly evolving technology.
• High ownership mindset focused on responsible AI adoption, production reliability, solution quality, and measurable business outcomes.

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