Machine Learning Engineer (Governance ML Platform)

FranceRemoteFull-timemid

Machine Learning EngineerData

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer (Governance ML Platform) based in France.

Technical Tools

Machine Learning EngineerData

This is a fully remote EMEA opportunity focused on building production-grade machine learning infrastructure for AI governance and trust.
You will help develop the ML foundation supporting next-generation AI agent capabilities built around PostgreSQL.
The role spans model serving, evaluation, red-teaming, monitoring, retraining, and continuous model improvement.
You will turn ML research and experimentation into reliable, low-latency production systems with strong operational controls.
A key part of the role is creating feedback loops that use telemetry and audit data to improve governance models over time.
You will work on security-sensitive systems where reliability, traceability, and responsible deployment are essential.
This position is ideal for an experienced ML engineer who enjoys combining applied machine learning, infrastructure, security, and emerging agent technologies.

Own the machine learning infrastructure supporting governance models, including model serving, evaluation, monitoring, red-teaming, and continuous improvement.
Build and maintain low-latency model-serving systems and evaluation pipelines designed for production workloads.
Develop and manage model registries and supporting infrastructure for reliable model lifecycle management.
Translate research and experimental models into production systems, including optimization techniques such as quantization and distillation to achieve low-latency inference.
Build drift-monitoring and shadow-deployment capabilities to identify changes in model behavior and validate new versions safely.
Develop telemetry-to-training pipelines that turn production signals and audit data into actionable inputs for model improvement.
Design and maintain red-team testing infrastructure, including attack orchestration, scorecards, regression suites, and CI-based quality gates.
Establish continuous self-improvement workflows covering dataset curation, retraining, validation, and controlled model rollout.
Implement appropriate safeguards and deployment controls to support secure and auditable machine learning operations.
Collaborate across engineering and AI-focused teams to advance infrastructure for AI agents, governance, trust, and retrieval capabilities.
Contribute to a high-quality engineering culture through documentation, testing, monitoring, and continuous improvement.

Requirements

~1 min read

4–7 years of professional experience in machine learning software engineering, with hands-on experience building LLM-based or agentic systems.
Strong production experience with ML model serving, particularly low-latency inference, quantization, and model distillation.
Proven experience building evaluation pipelines and working with model registries.
Experience designing red-team or adversarial testing frameworks, including regression testing and CI-gated workflows.
Practical experience with model drift monitoring and shadow deployment strategies.
Strong SQL skills and solid working knowledge of PostgreSQL.
Security-first mindset and experience developing or operating systems in audited, compliance-sensitive, or security-conscious environments.
Strong software engineering and problem-solving skills, with the ability to take ML systems from experimentation through reliable production deployment.
Ability to design scalable infrastructure and establish robust processes for model evaluation, monitoring, and continuous improvement.
Strong communication and collaboration skills within distributed, cross-functional engineering environments.
Experience building automated retraining loops using production telemetry or audit data is a plus.
Familiarity with Model Context Protocol (MCP), tool registries, and AI agent orchestration patterns is an advantage.
Experience developing PostgreSQL extensions in C or Rust, or contributing to the PostgreSQL ecosystem, is a plus.