ML Ops Engineer

Job Description

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