AI Native Engineer

Accenture France; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Accenture France is seeking an AI Engineer (Agentic/Applied) to design, build, and deploy production-grade agentic AI systems across the enterprise tech stack. You will collaborate with client engineering teams, lead design sessions, and create scalable patterns and accelerators for future engagements.

This role emphasizes hands-on production work, eval harnesses, and observability, with direct exposure to leading LLM providers and cloud-native architectures.

Qualifications

• Three production agentic AI systems shipped in four years.
• Minimum 2–5 years software engineering in production environments.
• Experience with agentic orchestration frameworks at production depth.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code.

Responsabilités

• Design and build production-grade agentic systems end-to-end.
• Own RAG pipelines: embeddings, chunking, vector search.
• Integrate across LLM providers with routing, cost, and latency management.
• Implement LLMOps in production: eval harnesses, prompt versioning, observability.
• Work with client engineering teams through workshops, PoCs, and architecture walkthroughs.
• Build reusable patterns and accelerators for future engagements.
• Define metrics for agent accuracy, latency, security, and cost; present business insights.

Connaissances

Agentic AI

Production systems

RAG pipelines

LLMOps

LangGraph

CrewAI

AutoGen

OpenAI

Vertex AI

Kubernetes

Docker

CI/CD

Terraform

Helm

Python

Java

Outils

LangSmith

Braintrust

Description du poste

You build the systems thatactually makeAI 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 Engineerprogramme.

Key Responsibilities

• Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets
• Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management
• ImplementLLMOpsin production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs
• Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster
• Define and use metrics to measure agent accuracy, latency, security, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms

#LI-EU

2 to 5 years of software engineering experience in production environmentsMinimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiableDemonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent — at production depth, not tutorial levelDirect experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffsRAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineeringLLMOpsfundamentals: eval harness design, prompt versioning, and production observabilityCloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, andIaC(Terraform or Helm)Strong Python; Java or equivalent backend language acceptable; production debugging and observability experienceQuality 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