Data Science & ML Ops Engineer

They want - Candidate with strong experience in understanding of Google/Azure and Spark/Python and MLOPs in general.

Candidate who has played both data scientist and ML engineer role will be ideal. But even if they are strong ML engineer with fair knowledge of Data science is ok

1. Job Title: Data Science & ML Ops Engineer

Location: SF Bay Area- Primary: Concord CA(Need only locals) & Secondary: Phoenix (Need only locals)

Key Responsibilities

• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments (e.g., Google Cloud Platform, AWS, Azure).
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
• Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications

• 10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.
• Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
• Solid understanding of software engineering principles and DevOps practices.

Ability to communicate complex technical concepts to non-technical stakeholders.