AI Native Software Engineer

Accenture•St. Louis, MO

2d•$80,400 - $316,300•Hybrid

About The Position

Accenture is a forward-thinking services company at the forefront of AI-native engineering. We partner with enterprise clients to design, build, and run production systems in which AI is a first-class engineering capability, engineered into the product and into the way our teams build it. Our engineers embed deeply with customers, own outcomes end to end, and move work past experimentation into operational reality. This role involves partnering directly with client technology leaders, acting as both engineer and trusted advisor. You will turn ambiguous requirements into architectures, executable increments, and measurable outcomes, then get them into production. Often these are net-new platforms that have to be stitched into a client's existing estate alongside our ecosystem partners. Travel may be required for this role, varying from 25% to 75% depending on business need and client requirements.

Requirements

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 at least one major language such as 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, 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 1 years 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. (If Associate's Degree, must have minimum 6 years work experience)

Nice To Haves

Built multi-agent orchestrations using frameworks such as LangGraph, Crew AI, the Claude Agent SDK, or the OpenAI SDK.
A public repository, portfolio, or open-source contribution featuring agents, tools, or plugins you built yourself.
Designed model and provider abstraction layers covering routing, fallback, latency, throughput, and cost management across multiple AI providers.
Defined enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry and stream-based architectures.
Relevant cloud, security, or AI certifications.
Delivered AI-native solutions across more than one industry (for example financial services, healthcare, retail), adapting workflows to domain-specific processes and constraints.
Experience applying AI-assisted engineering to modernize an existing legacy estate.
Driven execution across multiple concurrent workstreams while holding quality, delivery, and alignment with client outcomes.

Responsibilities

Own the Whole System: 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.
Engineer Cloud-Native Platforms: 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.
Own DevSecOps and Production Engineering: 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 the transactional, streaming, analytical, and unstructured data flows the solution needs, across relational and non-relational stores, object storage, event platforms, and pipelines.
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 Production AI and Agentic Systems: 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, together with the evaluation frameworks, AI observability, and production safeguards that keep probabilistic components trustworthy inside deterministic enterprise systems.
Engineer With AI: Use AI-native development techniques throughout the lifecycle, including code generation, testing, debugging, modernization, documentation, analysis, and operations, and design engineering workflows in which agents can safely perform bounded development and operational tasks.
Continuously identify where AI materially improves engineering throughput, quality, or system operations, and establish the context, tools, permissions, evaluations, and human controls that make it safe.
Lead Engineering Outcomes: Lead multidisciplinary teams spanning software, cloud, platform, security, data, and AI engineering; set engineering standards and develop the engineers around you.
Facilitate architecture and engineering sessions with senior client technologists, and communicate trade-offs, risks, and recommendations clearly to both technical and executive audiences.
Turn Delivery Into Reuse: 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.