Advanced AI Full Stack Engineer

Accenture•Kirkland, WA

10d•Onsite

About The Position

We are at the forefront of a new era in enterprise AI — one that moves beyond isolated models and experiments toward fully governed, production-grade AI systems. Our Data & AI practice brings together more than 45,000 professionals dedicated to helping clients build, deploy, and operate AI at scale. We design and engineer the platforms, runtimes, and developer tooling that make autonomous AI agents a reliable reality for the world's largest organisations. As an Advanced AI Full Stack Engineer, you will design and build the foundational systems that power AI agent platforms — from agent orchestration runtimes and sandboxed execution environments to inference routing layers, SDK tooling, and developer-facing APIs. You are a software engineer first, with deep fluency in Python and Node.js, who thrives at the intersection of distributed systems, AI infrastructure, and developer experience. You will work across the full platform stack — from CLI tooling and event-streaming protocols to multi-tenant Kubernetes-based execution environments — shipping production systems that other engineers and AI agents depend on.

Requirements

Bachelor's degree (or equivalent minimum 12 years work experience, or minimum 6 years' work experience with Associate's degree) in Computer Science, Computer Engineering, or a related field.
Minimum of 2 years of experience with Python and/or Node.js/TypeScript building production backend services or platform tooling.
Minimum of 1 year of experience building or integrating with AI/LLM systems, agent frameworks, or AI developer tooling.
2+ years of experience in one or more of: agent orchestration frameworks, inference serving infrastructure, sandboxed execution environments, or multi-tenant platform engineering.
Hands-on experience with async Python frameworks (FastAPI, asyncio), containerisation and Kubernetes, event-streaming protocols (WebSocket, SSE, gRPC), and vector/relational databases.
Familiarity with AI agent protocols (MCP, ACP, A2A), OpenTelemetry instrumentation, and modern AI framework ecosystems (LangGraph, OpenAI Agents SDK, Anthropic Claude SDK).

Nice To Haves

4+ years of experience with Python and Node.js/TypeScript, with a strong track record of building and shipping developer-facing platforms, SDKs, or APIs.
Master's or PhD in Computer Science, Computer Engineering, or a related field is a plus but not required.

Responsibilities

Design and build agent orchestration runtimes — stateful execution loops that coordinate tool discovery, model inference, approval gates, and context management.
Implement sandboxed execution environments with declarative policy enforcement (network egress, filesystem, compute quotas) that isolate agent workloads at the infrastructure level.
Develop pluggable provider interfaces so that sandbox backends (container-based or microVM-based) are swappable without changing agent code.
Build Python and Node.js/TypeScript SDKs and CLIs that give developers first-class interfaces for authoring, validating, and running AI agents locally and in enterprise environments.
Design REST, gRPC, and event-streaming APIs (WebSocket, SSE) that serve as the communication backbone between agent runtimes, IDE integrations, and platform services.
Implement framework adapters that normalize event streams from multiple AI frameworks into a unified platform event model, enabling consistent observability and governance regardless of the underlying agent framework.
Build and maintain intelligent inference routing layers that intercept model API calls and dispatch them to on-premise or cloud model endpoints based on data-sovereignty, cost, and capability policies.
Engineer multi-tier memory architectures spanning in-process working memory, cross-session relational stores, vector databases for semantic retrieval, and version-controlled procedural pipelines — each backed by swappable provider interfaces.
Implement ephemeral credential injection and RBAC-scoped data access so agents operate under least-privilege principles without long-lived secrets in agent code.
Instrument platform components with distributed tracing (OpenTelemetry), cost attribution, and P50/P95/P99 latency metrics exportable to standard observability backends.
Build CI/CD governance tooling — static validation pipelines that enforce schema correctness, separation-of-duty rules, and regulatory constraints before agent packages are promoted to production registries.
Implement human-in-the-loop approval gates and audit-trail mechanisms compatible with enterprise compliance requirements.
Work closely with cross-functional teams — AI researchers, product managers, security engineers, and enterprise architects — to align platform capabilities with real-world agent use cases.
Provide technical guidance on platform architecture decisions, code reviews, and engineering best practices across the team.
Communicate architectural trade-offs and platform roadmap decisions clearly to both technical and non-technical stakeholders.