AI Native Software Engineer

Accenture PLC; Washington (District of Columbia); On-site; USD 180,000 - 240,000; Full time

Job summary

Accenture is seeking an AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI systems across the enterprise tech stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns that scale beyond engagements.

This role sits at the heart of the AI engineering talent market, offering breadth across industries and leading with governance, cost controls, and observability.

Qualifications

• Significant years of software engineering experience in production environments.
• Hands-on experience designing and deploying agentic AI solutions in production.
• Production depth with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) or equivalent.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.
• RAG pipeline ownership: embeddings, chunking, vector databases, context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, production observability.
• Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, IaC (Terraform or Helm).
• Strong Python; Java or equivalent backend language; production debugging and observability.

Responsibilities

• Architect and govern production-grade agentic systems at enterprise scale across environments.
• Define RAG pipeline standards and embedding strategies; set quality benchmarks.
• Set multi-LLM integration standards across providers (OpenAI, Anthropic, Vertex AI, open-source).
• Own LLMOps at programme scale: eval strategy, prompt governance, observability, cost controls.
• Lead client engineering engagements at senior level; facilitate architecture design sessions and PoCs.
• Publish reusable patterns, accelerators, and standards to scale across engagements.
• Define measurement framework for agentic system quality and present AI impact to senior stakeholders.

Skills

Production software engineering

Agentic AI design

Python

Backend engineering

Observability

Production debugging

Client engagement at senior level

Tools

Kubernetes

Docker

microservices

serverless

CI/CD

Terraform / Helm (IaC)

LangGraph / CrewAI / AutoGen

OpenAI / Anthropic / Vertex AI APIs

Job description

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

This role sits at the heart of the AI engineering talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

#LI-MP

Key Responsibilities

• Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability

• Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable

• Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models

• Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems

• Lead client engineering engagements at senior level — facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams

• Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp‑up time on new client engagements

• Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme‑level AI impact in business terms to senior client stakeholders

Basic Qualifications

• Significant years of software engineering experience in production environments

• Practical hands‑on experience designing and deploying agentic AI solutions in a production environment — non‑negotiable

• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level

• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs

• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering

• LLMOps fundamentals: eval harness design, prompt versioning, and production observability

• Cloud‑native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)

• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience

• Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure

• People lead responsibilities: experience managing, developing, and performance‑managing a team of engineers; setting individual development plans and conducting career conversations

Saint‑Herblain

Brest

Egalité des chances face à l'emploi

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