Overview
Skills
Job Details
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 ONLY (San Leandro Preferably)
Duration: Longterm
9+ Years of experience reqruired
Overview
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.
Max | KLNtek
Email:
324 E Foothill Blvd, Ste 206, 91006 Arcadia, California