AI Engineer

Next Ventures; France; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Next Ventures is seeking an AI Engineer to advance AI capabilities across engineering and operations in a European remote setting. The role emphasizes designing, prototyping, and delivering scalable AI solutions that address real-world problems.

You will work across infrastructure and platform stacks, build reusable libraries and prompts, evaluate models and tooling, and drive adoption through governance and measurable outcomes.

Responsabilités

• Identify, scope, and prototype AI-driven solutions for engineering and operational workflows — code generation, automated documentation, incident triage, log analysis, capacity forecasting, and intelligent runbooks.
• Design and build retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM integrations against internal knowledge bases, codebases, and observability data.
• Develop reusable libraries, SDKs, prompts, and templates that allow other teams to safely adopt AI capabilities with minimal friction.
• Evaluate, benchmark, and select foundation models, vector stores, orchestration frameworks, and supporting infrastructure based on cost, latency, accuracy, and security.

Description du poste

AI Engineer

Remote - Europe

The successful candidate will identify high-value opportunities to apply AI and large language models (LLMs) to real engineering and operational problems, build the reference implementations and tooling that prove those opportunities out, and then drive adoption across teams through enablement, governance, and measurable outcomes. You will work fluently across a broad spectrum of the infrastructure and platform technology stack, meeting teams where they are rather than imposing a single approach.

Key Responsibilities

AI Solution Design & Engineering

• Identify, scope, and prototype AI-driven solutions for engineering and operational workflows — code generation, automated documentation, incident triage, log analysis, capacity forecasting, and intelligent runbooks.
• Design and build retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM integrations against internal knowledge bases, codebases, and observability data.
• Develop reusable libraries, SDKs, prompts, and templates that allow other teams to safely adopt AI capabilities with minimal friction.
• Evaluate, benchmark, and select foundation models, vector stores, orchestration frameworks, and supporting infrastructure based on cost, latency, accuracy, and security.