Machine Learning Engineer

Job Title: MLOps Engineer (Long Term Contract)

Engagement Type: Contract Service Worker (CSW)

Location: REMOTE

Start Date: ASAP

Role Overview

We are seeking a hands-on MLOps Engineer to support the deployment, automation, and operationalization of machine learning solutions in a production environment.

This is a delivery-focused individual contributor role, working closely with Data Scientists and Engineering teams to ensure ML models are reliably deployed, monitored, and maintained. The ideal candidate will bring strong experience in production ML systems, CI/CD, and workflow orchestration.

Experience with utility or energy domain data is a strong plus.

Key Responsibilities

• Deploy and support production ML workloads, including environment setup, dependency management, and configuration
• Build and maintain end-to-end ML pipelines (model handoff → deployment → retraining)
• Manage model lifecycle processes including versioning, promotion, and traceability
• Orchestrate workflows using Databricks Jobs / Workflows
• Implement and maintain CI/CD pipelines for ML systems (source control, containerization)
• Enable experiment tracking and governance using tools like MLflow
• Monitor deployed models and pipelines; troubleshoot and resolve production issues
• Collaborate with Data Scientists to productionize models (not focused on research/model development)

Required Skills & Experience

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

Preferred Qualifications

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

Engagement Expectations

• Hands-on, individual contributor role (no advisory-only profiles)
• Focus on deployment, automation, and reliability of ML systems
• Ability to work independently with minimal onboarding
• Close collaboration with internal Data Science and Engineering teams