Partnering with Data Science to translate experimental models into scalable, production-grade ML systems.
Designing and build high-performance, low-latency model-serving architectures and infrastructure.
Defining and enforce best practices for ML productionization, including coding standards, testing, and deployment strategies.
Leading cross-functional collaboration between Data Science, Engineering, and Operations as the primary technical interface.
Defining and implementing model performance monitoring (e.g., drift detection, data quality, inference metrics) to ensure reliability in production.
Driving innovation by evaluating new tools, frameworks, and methodologies, and promoting their adoption across the organization.
Developing and maintain technical documentation, system designs, and operational runbooks.

5+ years of experience in ML Engineering, MLOps, or closely related roles.
Proven experience deploying and maintaining machine learning models in production at scale.
Strong Python skills, including experience with data and ML ecosystems.
Familiarity with LLMs, RAG, and emerging AI paradigms (e.g., agent-based systems).
Hands-on experience with cloud platforms (e.g., AWS) for building and operating ML systems.
Experience with containerization (Docker) and building production-ready services.
Demonstrated ability to lead technical projects and make architectural decisions.

25 days of annual vacation leave plus 10 days of fully paid sick leave.
Fringes benefits: meal allowance, work-from-home allowance, gym allowance, private health insurance plan for yourself and 2 children.
1 day per month to tackle challenges inside the project, or test new ideas.
Tech training and human skills training and, budget for conferences.
Opportunity to travel and collaborate in our various office locations for workshops or team-building events.
A dynamic work environment that highly fosters a sense of fun while maintaining a productive atmosphere.
A competitive salary.

Senior ML Engineer

Key skills