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Accenture

Advises businesses and runs their technology

AI Native Software Engineer Manager/Senior Manager

Engineering LeadershipUpdated on Nov 11
Full-time

About the Job

Requirements

  • A minimum of 5 years of professional software engineering experience building and operating production systems.
  • A minimum of 5 years of hands-on experience spanning multiple stack layers, including front end or APIs through services, integration, persistence, and asynchronous processing.
  • A minimum of 5 years of programming experience in at least one major language such as Java, Python, Go, TypeScript/JavaScript, or C#.
  • A minimum of 5 years of hands-on production experience with AWS, Azure, or GCP, including containers and orchestration such as Docker and Kubernetes, and infrastructure as code such as Terraform or Helm.
  • A minimum of 5 years of experience designing and operating CI/CD pipelines through production, including automated testing and integrated security controls.
  • A minimum of 1 year of hands-on experience designing, building, and deploying AI-enabled or agentic systems in production or near-production environments, including model integration, tool use, retrieval, context engineering, orchestration, and evaluation.
  • A minimum of 1 year of experience applying AI-native engineering tools and workflows to day-to-day software development, including code generation, automated testing, debugging, modernization, and documentation.
  • A minimum of 5 years of experience leading engineering teams or significant technical workstreams, including leading client-facing technical discussions, workshops, or delivery sessions under ambiguity.
  • A bachelor's degree in Computer Science, Engineering, or equivalent, or equivalent work experience of at least 12 years. Candidates with an associate's degree must have at least 6 years of work experience.

Responsibilities

  • Design and build production systems across front end, APIs, backend services, integration, data, infrastructure, and AI components, making architectural decisions across these concerns together.
  • Diagnose production issues in application code, networks, infrastructure, identity, data, or AI workloads.
  • Design and deploy workloads using containers, Kubernetes, serverless, managed cloud services, and infrastructure as code such as Terraform or Helm, building for elasticity, resilience, and recoverability.
  • Use the native capabilities of AWS, Azure, or GCP, including cloud networking, compute, storage, identity, secrets, service communication, and workload isolation.
  • Build the automated path from commit to production, including CI/CD, automated testing, security scanning, artifact management, infrastructure deployment, policy enforcement, and release controls.
  • Apply secure-by-design practices covering identity, least privilege, secrets, encryption, dependency and supply-chain security; instrument logs, metrics, traces, and SLOs; and lead incident response and root-cause analysis.
  • Design and implement transactional, streaming, analytical, and unstructured data flows across relational and non-relational stores, object storage, event platforms, and pipelines.
  • Design the data and context architecture for AI systems, including retrieval, embeddings, metadata, knowledge sources, and context management, with quality, lineage, access, retention, and privacy engineered into it.
  • Design and implement AI capabilities using frontier and enterprise models, including OpenAI, Anthropic, Microsoft, Google, and AWS models, integrating them into live enterprise systems and production workflows.
  • Build agentic systems with tools, memory, context, planning, human intervention, and policy-controlled execution, together with evaluation frameworks, AI observability, and production safeguards.
  • Use AI-native development techniques throughout the lifecycle, including code generation, testing, debugging, modernization, documentation, analysis, and operations; design engineering workflows in which agents can safely perform bounded development and operational tasks.
  • Identify where AI materially improves engineering throughput, quality, or system operations, and establish the context, tools, permissions, evaluations, and human controls needed to make it safe.
  • Lead multidisciplinary teams across software, cloud, platform, security, data, and AI engineering; set engineering standards and develop engineers.
  • Facilitate architecture and engineering sessions with senior client technologists, and communicate trade-offs, risks, and recommendations to technical and executive audiences.
  • Convert lessons from delivery and production failures into reusable patterns, tooling, automation, and standards that influence internal assets and client roadmaps.
  • Contribute to internal communities of practice around AI-native and agentic engineering.
  • Travel as required, with the amount varying from 25% to 75% depending on business need and client requirements.

Desired Qualifications

  • Build multi-agent orchestrations using frameworks such as LangGraph, Crew AI, the Claude Agent SDK, or the OpenAI SDK.
  • Have a public repository, portfolio, or open-source contribution featuring agents, tools, or plugins built by the candidate.
  • Design model and provider abstraction layers covering routing, fallback, latency, throughput, and cost management across multiple AI providers.
  • Define enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry and stream-based architectures.
  • Hold relevant cloud, security, or AI certifications.
  • Deliver AI-native solutions across more than one industry, adapting workflows to domain-specific processes and constraints.
  • Apply AI-assisted engineering to modernize an existing legacy estate.
  • Drive execution across multiple concurrent workstreams while maintaining quality, delivery, and alignment with client outcomes.

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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