AI-Native Systems Lead Engineer

Accenture PLC

Charlotte (NC)

On-site

USD 94,000 - 316,000

Full time

9 hours ago

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Job summary

Accenture is seeking a senior software engineer to design and operate end-to-end production systems across frontend, APIs, data, cloud, and AI components, owning architecture and delivery from concept to production.

You will lead cross‑functional teams, shaping AI-enabled platforms on AWS/Azure/GCP, implementing CI/CD, security, and observability, and driving scalable, repeatable engineering practices at scale.

Qualifications

5+ years of professional software engineering experience building and operating production systems.
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 years 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 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. (If Associate's Degree, must have minimum 6 years work experience)

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.
Design and implement AI capabilities using frontier and enterprise models, 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 safeguards.