Senior AI Native Engineer
Remote • Paris
Posted 27d ago
Job Location
Paris
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Agent Engineer
About the job
You will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. This role involves working directly with client engineering teams, leading technical design sessions, and building reusable patterns and accelerators that scale beyond individual engagements. The focus is on shipping multi-agent systems in production, owning the evaluation harness, and understanding system failures in real-time enterprise environments. You will architect and govern production-grade agentic systems at enterprise scale, defining standards for RAG pipelines, multi-LLM integration, and LLMOps. This position offers broad exposure across industries and technology stacks, with opportunities for vendor fellowship access and a direct pathway to advanced engineering programs.
Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration, RAG pipelines, policy-based routing, memory management, and lifecycle observability.
- Define RAG pipeline standards, including chunking and embedding strategies, quality benchmarks, and metric-backed tradeoff decisions.
- Set multi-LLM integration standards for vendor-agnostic architecture, fallback routing, and cost governance across providers like OpenAI, Anthropic, and Vertex AI.
- Own LLMOps at program scale, covering eval strategy, prompt governance, observability tooling, safety monitoring, and cost controls.
- Lead client engineering engagements, facilitating architecture design sessions, proof-of-concept delivery, and alignment with client technology leadership.
- Shape and publish reusable patterns, accelerators, and engineering standards to scale across the practice.
- Own the measurement framework for agentic system quality, defining metrics for accuracy, latency, safety, and cost, and presenting AI impact to stakeholders.
Requirements
- 8+ years of software engineering experience in production environments.
- Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment.
- Demonstrated experience with agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen at production depth.
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction and cost tradeoffs.
- Experience with RAG pipeline ownership, including embeddings, chunking strategy, vector databases, and context engineering.
- Proficiency in LLMOps fundamentals such as eval harness design, prompt versioning, and production observability.
- Cloud-native engineering maturity, including Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
- Strong Python skills; Java or equivalent backend language acceptable.
- Production debugging and observability experience.
- Experience managing, developing, and performance-managing a team of engineers, including setting development plans and conducting career conversations.