Senior AI / Platform Engineer

Location : Remote(EU)

Contract details : 12 months / freelance PFA or SRL

We are looking for a Senior AI / Platform Engineer to help productize Mosaic AI into a governed, multi-tenant, self-service platform.

In this role, you will design, build, and operationalize large-scale AI and MLOps systems, enabling data scientists and developers to leverage advanced AI capabilities securely and efficiently. You will work at the intersection of machine learning infrastructure, AI agent frameworks, and enterprise-grade governance, contributing directly to the evolution of the companys next-generation AI platform.

Key Responsibilities :

Design and build scalable AI platform components using Databricks Mosaic AI, ensuring a secure, governed, and multi-tenant environment.

Implement and maintain MLOps pipelines leveraging MLflow, Vector Search, and other Databricks ecosystem tools.

Develop, deploy, and optimize AI agentic architectures including multi-agent systems, orchestration layers, and human-in-the-loop workflows.

Collaborate with data scientists to fine-tune and deploy foundation models (OpenAI, Anthropic, etc.) in production environments.

Apply strong Python engineering skills (PyTorch, TensorFlow) to build and scale distributed ML workloads.

Define and enforce AI governance, monitoring, and performance standards across the platform.

Partner with cross-functional teams (Data Engineering, Security, Product) to ensure seamless integration and usability.

Required Skills & Experience :

Deep expertise in Databricks Mosaic AI, including MLflow integration and Vector Search.

Proven experience in AI platform engineering or MLOps architecture within enterprise-scale environments.

Strong programming experience in Python, with hands-on knowledge of PyTorch or TensorFlow.

Experience designing or implementing agentic AI systems (multi-agent frameworks, orchestration layers, human feedback loops).

Familiarity with foundation models (e.g., OpenAI GPT, Anthropic Claude) and fine-tuning or retrieval-augmented generation (RAG) pipelines.

Strong understanding of distributed systems, data pipelines, and model serving in production.

Excellent problem-solving skills and ability to work collaboratively in cross-functional teams.