ML Ops Engineer

Job summary

Develop and deploy predictive models and ML pipelines to drive fraud reduction and operational efficiency. Automate model training, monitoring, and lifecycle management within cloud environments using CI/CD workflows.

Job details

Position: ML Ops Engineer Location : Concord, CA Duration: Contract 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.

What you’ll do

Develop and deploy predictive models and ML pipelines to drive fraud reduction and operational efficiency. Automate model training, monitoring, and lifecycle management within cloud environments using CI/CD workflows.

Requirements

Requires strong proficiency in Python, SQL, and major ML libraries along with experience in cloud platforms and containerization. Candidates should be familiar with data engineering tools and software engineering principles.

Listed skills

• Kubernetes · Preferred
• SQL · Preferred
• CI/CD · Preferred
• Docker · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• ML Ops
• Vertex AI
• MLflow
• Kubeflow
• Docker
• Kubernetes
• Scikit-learn
• XGBoost
• TensorFlow
• PyTorch
• CI/CD
• Airflow
• Spark
• AutoML

Job areas

• Data & Analytics
• Technology
• Software
• Engineering
• Consulting