Senior MLOps Engineer (ML Workflows Engineering)
Core
Designing tools, automation, and pipelines to streamline machine learning operations (MLOps) and enable the development of ML models and intelligent agents within developer IDEs.
Role type
Senior MLOps Engineer (ML Workflows Engineering)
Builds
Tools, automation, and workflows for ML infrastructure; monitoring, logging, and tracing systems; end-to-end ML pipelines.
Domain
Developer tools / AI / Machine Learning Infrastructure
Deliverable
infrastructure
Required skills
MLOps tooling, Kubernetes, Cloud providers (GCP, AWS), ML orchestration frameworks, CI/CD systems, Python, distributed systems, GPU clusters
Preferred skills
ML orchestrators (ZenML, Dagster, Airflow), Python backend services, experiment tracking (Weights & Biases, MLflow, Langfuse), LLM inference frameworks (vLLM, DeepSpeed, TensorRT), NLP theory, Java/Kotlin
Technologies
Kubernetes, GCP, AWS, GitHub Actions, JetBrains TeamCity, ZenML, Dagster, Airflow, Weights & Biases, MLflow, Langfuse, vLLM, DeepSpeed, TensorRT
Responsibilities
Build tools and automation to simplify infrastructure-heavy tasks; Develop robust monitoring, logging, and tracing systems; Design and maintain end-to-end ML pipelines; Work with large-scale distributed systems and GPU clusters; Collaborate with product and development teams to transform goals into scalable systems; Optimize workflows for reproducibility, scalability, and cost-efficiency.
Seniority
Senior, hands-on IC