Machine Learning Engineer (Governance ML Platform)

Jobgether SRL; Schweiz; Remote; CHF 120,000 - 180,000; Full time

Benefits offered by this job

100% remote work in the EMEA region

Hands-on ownership of production ML‑in

Wellness Fridays

Job summary

A partner company in Switzerland is seeking a Machine Learning Engineer to build production-grade ML infrastructure for AI governance and trust. The role is fully remote within the EMEA region and focuses on model serving, evaluation, monitoring, red-teaming, and continuous improvement.

You will work on security-sensitive systems with emphasis on reliability, traceability, and responsible deployment, turning research into robust production systems around PostgreSQL-powered components.

Qualifications

• 4–7 years of professional experience in machine learning software engineering.
• Hands-on experience building LLM-based or agentic systems.
• Strong production experience with ML model serving, low-latency inference, quantization, and distillation.
• Proven experience building evaluation pipelines and working with model registries.
• Experience designing red-team or adversarial testing frameworks with 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 in audited or security-conscious environments.
• Strong software engineering and problem-solving skills for production deployment.
• Ability to design scalable infrastructure for model evaluation and monitoring.

Responsibilities

• Own the ML infrastructure for governance models, including serving, evaluation, monitoring, red-teaming, and continuous improvement.
• Build low-latency model-serving systems and evaluation pipelines for production workloads.
• Develop and manage model registries and lifecycle infrastructure.
• Translate research models into production with optimization techniques like quantization and distillation.
• Create drift-monitoring and shadow-deployment capabilities to validate new versions safely.
• Develop telemetry-to-training pipelines from production signals and audit data.
• Design red-team testing infrastructure, scorecards, regression suites, and CI-based gates.
• Establish continuous self-improvement workflows for data curation, retraining, and rollout.
• Implement safeguards and deployment controls for auditable ML operations.
• Collaborate across engineering teams on AI agents, governance, and retrieval capabilities.
• Contribute to a high-quality engineering culture with documentation, testing, and monitoring.

Skills

ML model serving

Low-latency inference

PostgreSQL

SQL

Model drift monitoring

Red-team / adversarial testing

CI/CD

Security/compliance

Distributed engineering

Python

C/U Rust PostgreSQL extensions

Tools

Docker

Git

Job description

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 Switzerland.

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

• 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.

Benefits

• 100% remote work within the EMEA region.
• Opportunity to work on emerging AI agent infrastructure and machine learning governance technologies.
• Hands‑on ownership of production ML infrastructure spanning serving, evaluation, security testing, and continuous improvement.
• Opportunity to work at the intersection of machine learning, AI agents, PostgreSQL, security, and enterprise technology.
• Access to health and wellness resources, including CuraLinc.
• Wellness Fridays available through December 2026.
• Additional region‑specific benefits and perks depending on location.
• Remote‑first environment supporting collaboration across an international engineering organization.
• Opportunity to contribute to the development of secure, reliable, and enterprise‑ready AI technologies.