Ampstek
W2 Candidate :: Local CA Only :: Data Science & ML Ops Engineer
Ampstek, San Francisco, California, United States, 94199
Overview
Position: Data Science & ML Ops Engineer. Location: SF Bay Area ONLY (San Leandro preferred) (5 days onsite). Duration: Contract (W2 Candidate Only). Responsibilities
Develop predictive models using structured and unstructured data across multiple business lines to drive 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., GCP, 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. Demonstrate 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. Qualifications
Experience understanding of Google/Azure and Spark/Python with ML Ops. Record of roles spanning data science and ML engineering; strong ML engineering with data science knowledge considered. Proficiency in Python, SQL, and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch). Experience with cloud platforms and containerization (Docker, Kubernetes). Familiarity with data engineering tools (Airflow, Spark) and ML Ops frameworks. Strong communication skills and ability to explain technical concepts to stakeholders.
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Position: Data Science & ML Ops Engineer. Location: SF Bay Area ONLY (San Leandro preferred) (5 days onsite). Duration: Contract (W2 Candidate Only). Responsibilities
Develop predictive models using structured and unstructured data across multiple business lines to drive 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., GCP, 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. Demonstrate 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. Qualifications
Experience understanding of Google/Azure and Spark/Python with ML Ops. Record of roles spanning data science and ML engineering; strong ML engineering with data science knowledge considered. Proficiency in Python, SQL, and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch). Experience with cloud platforms and containerization (Docker, Kubernetes). Familiarity with data engineering tools (Airflow, Spark) and ML Ops frameworks. Strong communication skills and ability to explain technical concepts to stakeholders.
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