Machine Learning Engineer (Governance ML Platform)

JobgetherAI governance

This is a fully remote EMEA opportunityFull-TimeMiddle

Salary not disclosed

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Job Details

Experience: 4–7 years of professional experience in machine learning software engineering
Required Skills: PostgreSQLSQLMachine Learning

Bring 4–7 years of professional experience in machine learning software engineering.
Have hands-on experience building LLM-based or agentic systems.
Bring production experience with ML model serving, especially low-latency inference, quantization, and model distillation.
Have experience building evaluation pipelines and working with model registries.
Have experience designing red-team or adversarial testing frameworks, including regression testing and CI-gated workflows.
Have practical experience with model drift monitoring and shadow deployment strategies.
Demonstrate strong SQL skills and working knowledge of PostgreSQL.
Have experience developing or operating systems in audited, compliance-sensitive, or security-conscious environments.
Be able to take ML systems from experimentation through reliable production deployment.
Be able to design scalable infrastructure and processes for model evaluation, monitoring, and continuous improvement.
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.

Own ML infrastructure for governance models, including model serving, evaluation, monitoring, red-teaming, and continuous improvement.
Build and maintain low-latency model-serving systems and evaluation pipelines for production workloads.
Develop model registries and infrastructure for model lifecycle management.
Translate research and experimental models into production systems, using techniques such as quantization and distillation for low-latency inference.
Build drift-monitoring and shadow-deployment capabilities to identify behavior changes and validate model versions.
Develop telemetry-to-training pipelines using production signals and audit data to improve models.
Design red-team testing infrastructure, including attack orchestration, scorecards, regression suites, and CI-based quality gates.
Establish workflows for dataset curation, retraining, validation, and controlled model rollout.
Implement safeguards and deployment controls for secure, auditable machine learning operations.
Collaborate across engineering and AI-focused teams on infrastructure for AI agents, governance, trust, and retrieval capabilities.

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