Data Science & ML Ops Engineer in San Francisco, CA | Locals required

Hello

Please check the below position and reply back with the details and updated resume if you are interested.

Job title: Data Science & ML Ops Engineer

Location: SF Bay Area Locals Only (San Leandro Preferably)

Duration: Longterm

Visa All Visa Expect (No Please)

Passport Number is mandatory

12+ Years of experience required

Looking for a candidate with strong experience in understanding of Google/Azure and Spark/Python and MLOPs in general. And a Candidate who has played both data scientist and ML engineer role will be ideal.

- Strong ML engineer exp Needed

Overview
Tachyon Predictive AI team seeking a hybrid Data Science & ML Ops Engineer to drive the full lifecycle of machine learning solutions from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.

Key Responsibilities

• Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.
• Leverage AutoML tools(e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment
• 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

Qualifications

• Strong proficiency in 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.