Design, develop, and test operating systems-level software, compilers, and network distribution software.
Set operational specifications, and formulate and analyze software requirements.
May design embedded systems software.
Building and maintaining ML Ops infrastructure for model deployment and monitoring.
Developing automated workflows for training, testing, and CI/CD of ML models.
Implementing best practices for reproducibility, scalability, and governance in ML pipelines.
Managing model versioning, performance tracking, and retraining strategies.
Driving innovation in ML Ops processes to improve efficiency and reliability.

Bachelor's Degree and 9 years of relevant exempt experience; Master's Degree and 7 years of relevant professional experience; Ph.D. and 4 years of experience.
Bachelor’s degree in Computer Science, Engineering, or related field.
Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch).
Hands-on experience with ML Ops tools (MLflow, Kubeflow, Airflow) and cloud platforms (AWS, Azure, GCP).
Expertise in containerization and orchestration (Docker, Kubernetes).
Experience with CI/CD pipelines and version control systems (Git).
Solid understanding of monitoring, logging, and alerting for ML systems.

Senior ML Ops Software Engineer

Key skills