Sr MLOps engineer || San Leandro (Bay area) || Onsite

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
100% Travel

Skills

Google Cloud Platform
Data Science
Docker
Documentation
Fraud
Good Clinical Practice
Collaboration
Continuous Delivery
Continuous Integration
Data Engineering
Amazon Web Services
Apache Spark
Regulatory Compliance
Software Engineering
Microsoft Azure
Operational Efficiency
ProVision
PyTorch
Python
Lifecycle Management
Machine Learning (ML)
Machine Learning Operations (ML Ops)
SQL
Artificial Intelligence
Bridging
Cloud Computing
DevOps
Kubernetes
MRM
TensorFlow
Testing
Training
Unstructured Data
Vertex
Workflow
XGBoost
scikit-learn

Job Details

Sr MLop engineer

San Leandro (Bay area)

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.

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