Jobgether is looking for a (Senior) Machine Learning Platform/Ops Engineer to build and maintain robust infrastructure for production-ready ML models at scale. You will collaborate closely with data scientists and software engineers to design reproducible pipelines, operationalize models, and ensure high availability and performance. Your work will accelerate experimentation and innovation across our ML initiatives.

What You'll Do

Design, implement, and maintain scalable, containerized ML pipelines for model training, evaluation, and deployment across batch and real-time workflows.
Operationalize models at scale, building and managing ML services, APIs, and serving infrastructure using tools such as MLflow, SageMaker, and Feature Store.
Set up automated monitoring, observability, and alerting systems to ensure high availability, detect model/data drift, and maintain inference performance.
Develop internal libraries, templates, and platform tooling to improve reproducibility and simplify deployment workflows for all ML teams.
Implement CI/CD pipelines for model and data workflows using Docker, Terraform, Jenkins, or equivalent, and promote best practices across the organization.
Evaluate and adopt emerging MLOps technologies to enhance efficiency, scalability, and reliability of ML platforms.
Mentor junior engineers and foster cross-functional collaboration between engineering and data science teams.

What We're Looking For

2+ years of hands-on experience operationalizing, deploying, monitoring, and maintaining ML models at scale.
Strong proficiency with Python and ML libraries such as scikit-learn, LightGBM, PyTorch, or TensorFlow.
Solid SQL skills.
Experience with infrastructure-as-code and CI/CD systems (Docker, Terraform, Jenkins, or equivalent).
Experience with at least one major cloud provider (AWS, GCP, or Azure).
Knowledge of ML monitoring and logging, including model drift detection, data validation, and performance/feature tracking.
Excellent collaboration and communication skills, able to partner effectively across engineering and data science teams.

Nice to Have

Experience with feature stores, model versioning, or building internal ML platforms.

Technical Stack

Languages & Libraries: Python, scikit-learn, LightGBM, PyTorch, TensorFlow, SQL
Infrastructure & CI/CD: Docker, Terraform, Jenkins
Cloud Providers: AWS, GCP, Azure
MLOps Tools: MLflow, SageMaker, Feature Store

Team & Environment

You will collaborate closely with data scientists and software engineers and have opportunities to mentor junior engineers. We operate in a highly collaborative and forward-thinking environment that fosters collaboration and innovation.

Benefits & Compensation

Flexible remote work up to 5 days per week across Europe.
International, collaborative teams with opportunities to contribute ideas directly to platform development.
Educational budget to support continuous learning and skill development.
Participation in team events, including Hackathons.
Discounts on partner platforms for high-quality used cars.
Personal growth opportunities in a dynamic, fast-moving environment.