Senior AI Native Engineer (Agentic AI)

About the role

Accenture is hiring a Senior AI Native Engineer in Paris to design, build and deploy production-grade agentic AI systems across the full enterprise technology stack. The role works directly with client engineering teams, leads technical design sessions and builds reusable patterns and accelerators that scale beyond individual engagements.

Key responsibilities

• Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration, RAG pipelines, policy-based routing, memory management and programme-level observability.
• Define RAG pipeline standards across engagements, including chunking and embedding strategies, quality benchmarks and documented trade-offs.
• Set multi-LLM integration standards with vendor-agnostic architecture, fallback routing and cost governance across providers such as 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.
• Lead client engineering engagements at senior level, facilitate architecture design sessions, lead proof-of-concept delivery and align client technology leadership with delivery teams.
• Shape and publish reusable patterns, accelerators and engineering standards, and own the measurement framework for accuracy, latency, safety and cost metrics.

Required profile

• At least 8 years of software engineering experience in production environments.
• At least 1 year of hands-on experience designing and deploying agentic AI solutions in production (non-negotiable).
• Experience managing, developing and performance-managing a team of engineers, including setting development plans and career conversations.
• Quality of experience is weighted over years; having shipped three production agentic systems in four years is preferred.

Required skills

• Production-level experience with agentic orchestration frameworks such as LangGraph, CrewAI or AutoGen.
• Calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, with token management and cost awareness.
• RAG pipeline ownership: embeddings, chunking, vector databases and context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning and production observability.
• Cloud-native engineering: Kubernetes, Docker, microservices, serverless, CI/CD and IaC (Terraform or Helm).
• Strong Python; Java or an equivalent backend language is acceptable.