AI-Native Production Systems Engineering Manager

Accenture PLC

Scottsdale (AZ)

On-site

USD 94,000 - 316,000

Full time

3 hours ago

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Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Long-term disability

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

Accenture seeks a senior software engineer to design and operate production systems spanning frontend, APIs, data, and AI components, owning architecture and end-to-end delivery. You will partner with leaders to turn ambiguous requirements into production-ready architectures and measurable outcomes, embedding AI-enabled capabilities into enterprise workflows.

You will lead multi-disciplinary teams, apply secure design, and drive CI/CD practices across AWS/Azure/GCP environments while traveling

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 programming experience in Java, Python, Go, TypeScript/JavaScript, or C#.
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).
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 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.
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.
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.
Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience.

Responsibilities

Design and engineer production systems across front end, APIs, backend, data, 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).
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: design and implement data flows and context architecture for transactional, streaming, analytical, and unstructured data.
Design the data and context architecture AI systems depend on, including retrieval, embeddings, metadata, knowledge sources, and context management, with quality, lineage, access, retention, and privacy engineered in.
Build AI-enabled agentic systems with tools, memory, context, planning, human intervention, and policy-controlled execution, with evaluation frameworks and production safeguards.