AI-Native Engineering Manager: Build Production AI

Accenture PLC; Orlando (FL); On-site; USD 87,000 - 273,000; Full time

Benefits offered by this job

Medical, dental, vision, life

Long-term disability

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

Accenture is seeking a senior software engineer to design and operate end-to-end production systems across front-end, APIs, data, and AI components. You will own architecture, implement cloud-native platforms, and lead multidisciplinary teams while staying hands-on to inspect and improve implementations.

You will build AI-enabled capabilities, establish secure CI/CD pipelines, and apply secure-by-design practices across the stack.

Qualifications

• Minimum of 5 years of professional software engineering experience building and operating production systems.
• Minimum of 5 years of hands-on experience spanning multiple layers of the stack, including front end or API through services, integration, persistence, and asynchronous processing.
• Minimum of 5 years of hands-on production experience with a major cloud platform (AWS, Azure, or GCP), including containers and orchestration (Docker, Kubernetes) and infrastructure as code (Terraform, Helm, or equivalent).
• Minimum of 5 years of experience designing and operating CI/CD pipelines through to production, including automated testing and integrated security controls.
• Minimum of 1 year 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.
• Minimum of 1 year experience applying AI-native engineering tools and workflows to day-to-day software development, including code generation, automated testing, debugging, modernization, and documentation.
• 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.

Responsibilities

• Design and build production systems across front end, APIs, backend services, integration, data, infrastructure, and AI components, making architectural decisions across all of these concerns together.
• Diagnose production issues wherever they live: application code, networks, infrastructure, identity, data, or AI workloads.
• Design and deploy workloads using containers, Kubernetes, serverless, managed cloud services, and infrastructure as code (Terraform, Helm, or equivalent), 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: CI/CD, automated testing, security scanning, artifact management, infrastructure deployment, policy enforcement, and release controls.
• Apply secure-by-design practices (identity, least privilege, secrets, encryption, dependency and supply-chain security), instrument logs, metrics, traces, and SLOs, and lead incident response and root-cause analysis.
• Engineer Data and Context across transactional, streaming, analytical, and unstructured data flows; design data and context architecture AI systems depend on, including retrieval, embeddings, metadata, knowledge sources, and context management.
• Design and implement AI capabilities using frontier and enterprise models (OpenAI, Anthropic, Microsoft, Google, AWS, and others), integrated into live enterprise systems and production workflows.
• Build agentic systems with tools, memory, context, planning, human intervention, and policy-controlled execution, with evaluation frameworks and production safeguards.
• Engineer AI-native development techniques throughout the lifecycle, including code generation, testing, debugging, modernization, documentation, and operations.

Skills

Production systems

Cloud platforms

CI/CD pipelines

AI-enabled engineering

Leadership

Distributed architecture

Education

Bachelors in CS/Engineering or equivalent

Tools

Docker

Kubernetes

Terraform

Helm

Job description

Accenture is seeking a senior software engineer to design and operate end-to-end production systems across front-end, APIs, data, and AI components. You will own architecture, implement cloud-native platforms, and lead multidisciplinary teams while staying hands-on to inspect and improve implementations.

You will build AI-enabled capabilities, establish secure CI/CD pipelines, and apply secure-by-design practices across the stack.