Senior AI Engineer - from POC to Production (Python · LangGraph · RAG · Azure)

Project overview

Ever"T — Native AI, innovative, and hyper-growth tech consulting firm — is looking for a freelance Senior AI Engineer.

The Project

You will join the Data Science team of a large international group, reporting to the AI and Data Science department, with a global scope. The team leads the group's generative and agentic AI strategy and is looking for an Engineer capable of managing the entire chain: from scoping with business units to production deployment and run.

The project has two parts. The first involves internalizing AI solutions already built by external partners: integrating them into the group's Azure environment, managing updates, ensuring deployment reliability, and taking charge of operations. The second involves scoping and prototyping new use cases: identifying needs, verifying feasibility, building a POC, evaluating it, and then industrializing it.

Examples of current or upcoming topics:

• Automated processing of customer reviews, with automatic responses and escalation to support
• Email content creation assistant, to be enhanced with multi-country translation and localization
• CRM agent capable of recommending a customer communication strategy (content, channel, timing) based on campaign history, with a feedback loop

Responsibilities

Scoping and Prioritization

Design, Prototyping, and Evaluation

Industrialization and LLMOps

Collaboration and Transfer

Why Ever"T?

With us, you join a native AI community built on three founding pillars: Humility. Progress. Commitment.

You benefit from strong support (your Community Builder, your Project Facilitator), access to continuous training, regular events (T"Time, T"Coached, T"Cast), and the Diversified Employee program to develop your personal or business projects in parallel.

👉 www.ever-t.fr

• Lead workshops with business and product teams to qualify high-impact use cases
• Evaluate value, feasibility, data quality, and risks (security, compliance, ethics)
• Define KPIs, acceptance criteria, and a realistic roadmap
• Design suitable architectures: RAG, semantic search, agents and multi-agents, fine-tuning when relevant
• Develop clean and documented POCs in Python
• Set up experiments to measure quality, robustness, bias, costs, and latency
• Define application safeguards against risks specific to LLMs
• Transform validated prototypes into tested, versioned, and production-ready components
• Work with Data Engineering and MLOps teams on CI/CD, containerization, orchestration, and observability
• Implement monitoring and alerting: response quality, drift, latency, costs
• Document technical choices, runbooks, and operational procedures
• Participate in code reviews, demos, and team rituals
• Organize knowledge transfer to enable internal teams to become autonomous
• Proactively report risks, dependencies, and opportunities

Project details

• Start: flexible
• Remote: hybrid
• Location: Paris, France, FR
• Languages: French