Senior Software Engineer, ML Ops
Core
Architect and build infrastructure and automation in AWS and on-premises environments for ML application development and deployment.
Role type
Senior IC MLOps engineer
Builds
MLOps suite, development platforms, automated ML workflows
Domain
Cloud infrastructure, ML operations, DevOps
Deliverable
infrastructure
Required skills
Kubernetes, cloud computing platforms, observability and monitoring tools, DevOps principles, infrastructure-as-code, Python, multi-language systems, scalable backend architecture
Preferred skills
PyTorch, Scikit-learn, Airflow, Kubeflow, model lifecycle management, feature stores, model monitoring, CI/CD for ML, streaming data processing (Kafka, Flink, Spark Streaming), AI assistants (CoPilot, Cursor)
Technologies
AWS, Kubernetes, Airflow, Kafka, Flink, Spark Streaming, PyTorch, Scikit-learn, Kubeflow
Responsibilities
Lead system design and architectural discussions for the MLOps suite; Research, evaluate, and implement MLOps tools, frameworks, and best practices; Optimize ML workflows for efficient and reproducible model deployment and monitoring; Automate ML operations, including feature engineering pipelines and deployment strategies; Own development platforms serving internal customers; Enforce coding standards, conduct design reviews, mentor junior engineers, and lead technical initiatives
Seniority
Senior, hands-on IC with mentorship responsibilities
