Senior AI Native Engineer

Accenture France; Paris; Sur place; EUR 90 000 - 140 000; Plein temps

Résumé du poste

Accenture France seeks an AI Engineer (Agentic/Applied) to 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, offering breadth across industries and access to fellowship programs with leading tech companies

Qualifications

• 8+ years of software engineering in production environments.
• Hands-on experience designing and deploying agentic AI solutions in production.
• Experience with agentic orchestration frameworks and LLM APIs in production.

Responsabilités

• Architect and govern production-grade agentic systems at enterprise scale.
• Define RAG pipeline standards and multi-LLM integration standards.
• Own LLMOps programs: eval strategy, prompt governance, observability, and cost controls.
• Lead client engineering engagements and disseminate reusable patterns.

Connaissances

Python

Java

Kubernetes

Docker

LLMOps

LangGraph

CrewAI

OpenAI API

Outils

Kubernetes

Docker

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

• Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, andprogramme-level lifecycle observability
• Define RAG pipeline standards across engagements:establishchunking 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, fallbackroutingand cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models
• OwnLLMOpsatprogrammescale: eval strategy, prompt governance, observability tooling standards, safetymonitoringand cost controls across multiple concurrent systems
• Lead client engineering engagements at senior level —facilitatearchitecture 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; presentprogramme-level AI impact in business terms to senior client stakeholders

8+ 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 exposurePeople lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations