JetBrains - Senior MLOps Engineer (ML Workflows Engineering)
Requirements
• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow. • Developing infrastructure components and services using cluster solutions like Kubernetes. • The development of Python-based backend services. • Creating and maintaining ML pipelines, including legacy ones. • Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar. • We’d be especially thrilled if you have experience with: • LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT. • Writing and maintaining Python libraries used by internal (or external) ML engineers. • A strong theoretical background in NLP and transformer-based approaches. • Writing code in Java and/or Kotlin. • #LI-HYBRID#LI-MR1 • We process the data provided in your job application in accordance with the Recruitment Privacy Policy.
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. • We’ll be happy to have you on our team if you have: • Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks. • 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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