Data Science & ML Ops Engineer (CA Local Candidate Only)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Science & ML Ops Engineer in the SF Bay Area, focusing on predictive model development and ML pipeline maintenance. Contract duration is W2 only, requiring strong Python, SQL, and cloud experience with tools like MLflow and AutoML.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 10, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
W2 Contractor
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🔒 - Security clearance
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Compliance #Airflow #Observability #Monitoring #AWS (Amazon Web Services) #ML Ops (Machine Learning Operations) #Python #Data Science #Automation #TensorFlow #Kubernetes #DevOps #Cloud #Azure #MLflow #Documentation #ML (Machine Learning) #SQL (Structured Query Language) #Docker #Libraries #PyTorch #GCP (Google Cloud Platform) #AI (Artificial Intelligence) #Spark (Apache Spark) #Data Engineering #Deployment
Role description
Position: Data Science & ML Ops Engineer
Location : SF Bay Area ONLY (San Leandro Preferably) (5days onsite)
Duration: Contract(W2 Candidate Only)
Job Description::
• 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., 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
• 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.
Position: Data Science & ML Ops Engineer
Location : SF Bay Area ONLY (San Leandro Preferably) (5days onsite)
Duration: Contract(W2 Candidate Only)
Job Description::
• 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., 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
• 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.
