ML Ops Engineer

We are seeking a contract ML Ops Engineer to support the deployment, automation, and operationalization of machine learning solutions in a production environment. This role is hands-on and delivery-focused, working closely with data scientists and engineering teams to ensure ML models are reliably deployed, monitored, and maintained.

This engagement is best suited for a senior-level individual contributor with strong ML Ops and software engineering experience. Prior exposure to utility or energy industry data is a strong plus.

Primary Responsibilities

• Deploy and support production ML workloads, including environment setup, dependency management, and configuration
• Build and maintain end-to-end ML pipelines, from model handoff through deployment and retraining
• Manage model lifecycle processes, including versioning, promotion, and traceability using a model registry and feature store
• Orchestrate and schedule workflows using Databricks Jobs / Workflows
• Implement and maintain CI/CD pipelines for ML systems, including source control integration and containerized deployments
• Enable experiment tracking and governance using tools such as MLflow
• Monitor deployed models and pipelines; troubleshoot production issues and support continuous improvements
• Collaborate with data scientists to productionize models (this role does not require deep model research or experimentation ownership)

Required Skills & Experience

• 5+ years of experience in ML Ops, ML Engineering, with a strong focus on production ML
• Hands-on experience with Databricks for ML deployment and workflow orchestration
• Strong experience with CI/CD practices for ML or data platforms (e.g., GitHub, Docker)
• Experience with model registries, feature stores, and experiment tracking (MLflow or equivalent)
• Proficiency in Python and production-quality coding practices
• Familiarity with common ML libraries and frameworks (e.g., scikit-learn, XGBoost, TensorFlow, Spark MLlib)
• Experience working with distributed or parallel processing frameworks (Spark, Ray, Dask, joblib)

Preferred / Nice-to-Have

• Experience working with utility, energy, or operational analytics data
• Exposure to regulated or enterprise data environments
• Familiarity with cloud-based analytics or data platforms