Role: Data Science & ML Ops Engineer
Location: San Leandro, California
Qualifications:
- Strong proficiency in Python and SQL, with hands-on experience in leading ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Experience working with cloud platforms (GCP, AWS, Azure) and containerization technologies (Docker, Kubernetes).
- Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
- Solid understanding of software engineering principles and DevOps practices, including CI/CD.
- Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders.
Key Responsibilities:
- Design and develop predictive models using structured and unstructured data across more than 10 business lines, enabling fraud reduction, operational efficiency, and improved customer insights.
- Leverage AutoML platforms (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, automated documentation, and rapid deployment.
- Develop and maintain ML pipelines using tools such as MLflow, Kubeflow, or Vertex AI to streamline model lifecycle management.
- Automate model training, testing, deployment, and monitoring in cloud environments (GCP, AWS, Azure).
- Implement CI/CD workflows for model versioning, monitoring, retraining, and governance compliance.
- Monitor model performance with observability tools, ensuring adherence to model risk management (MRM), documentation, and explainability standards.
- Collaborate with engineering teams to provision containerized environments and support model scoring through low-latency APIs.
Regards
Praveen Kumar
Talent Acquisition Group – Strategic Recruitment Manager
praveen.r@themesoft.com| Themesoft Inc
