Senior MLOps Engineer developing machine learning tools and pipelines for JetBrains. Aiming to streamline MLOps and improve AI productivity for innovative teams.
Responsibilities
Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.
Requirements
Hands-on experience with modern MLOps tooling, including Kubernetes
A solid understanding of the ML lifecycle from idea to the customer-facing application
The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases
A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions
Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity
At least three years of Python experience writing clean, maintainable code in modern ML codebases.
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