Senior AI Engineer: Agentic Systems Lead (Remote)

Shift Technology; Paris; Hybrid; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Flexible remote and hybrid

Competitive salary

Learning & Development opportunities

Generous PTO and holidays

Mental health benefits

Volunteer days

Job summary

Shift Technology is seeking a seasoned Staff AI Engineer to anchor our AI capabilities. You will architect, prototype, and ship cutting-edge agentic workflows and tool integrations, collaborating with data scientists and product leaders to scale AI systems into production.

Responsibilities include moving prototypes to robust microservices on Azure, implementing MCP and RAG pipelines, and ensuring observability and latency targets.

Qualifications

• Hands-on experience building agentic AI systems and pipelines.
• Strong backend software engineering with Python.
• Experience with MCP, RAG patterns, vector pipelines, and structured outputs.
• Proficiency in productionizing AI components in cloud environments.
• Familiarity with MLOps, observability, and latency optimization.

Responsibilities

• Architect and build production-ready agentic workflows and tool-calling integrations.
• Migrate AI prototypes to robust microservices on Azure with low latency.
• Implement MCP, RAG pipelines, vector databases, and structured LLM outputs.
• Define and enforce AI quality, prompts, monitoring, and governance practices.
• Mentor squads, drive technical design reviews, and set coding standards.
• Benchmark models and tools, translating breakthroughs into value.

Skills

Agentic frameworks

LangGraph

LlamaIndex

OpenAI Agents SDK

Custom orchestration

Tools

Azure

Docker

Kubernetes

Terraform

Bicep

REST/OpenAPI

Job description

Shift Technology is seeking a seasoned Staff AI Engineer to anchor our AI capabilities. You will architect, prototype, and ship cutting-edge agentic workflows and tool integrations, collaborating with data scientists and product leaders to scale AI systems into production.

Responsibilities include moving prototypes to robust microservices on Azure, implementing MCP and RAG pipelines, and ensuring observability and latency targets.