Build tools, automation, and workflows to simplify infrastructure-heavy tasks.
Develop monitoring, logging, and tracing for ML workflows in production.
Design and maintain end-to-end ML pipelines for development, training, deployment.
Work with large-scale distributed systems, including GPU clusters.
Collaborate with product and development teams to translate goals into scalable systems.
Optimize workflows for reproducibility, scalability, and cost-efficiency.

🎯 Requirements

Hands-on experience with MLOps tooling: Kubernetes, GCP/AWS, and ML orchestration.
Strong ML lifecycle knowledge from idea to customer app.
Ability to own projects end-to-end from problem to design and delivery.
Customer-centric mindset; translate ML needs into scalable, maintainable architecture.
Experience with CI/CD systems (GitHub Actions, JetBrains TeamCity).
3+ years Python in modern ML codebases.

🎁 Benefits

Equal opportunity employer with an inclusive workplace
Hybrid work model

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