Machine Learning Engineer

You’ll design, deploy, and maintain ML pipelines on AWS and Databricks, automating the full model lifecycle from training to deployment and monitoring. You’ll implement CI/CD workflows for ML systems, ensure model reproducibility and versioning, optimize compute costs and performance, and collaborate closely with data scientists and engineers to bring models reliably into production.

• 3-5 years of experience in MLOps, data engineering, or a related field
• Strong Python skills, including writing production-grade, testable code
• Hands-on experience with AWS services (e.g., S3, SageMaker, Lambda, ECS/EKS, IAM)
• Solid working knowledge of Databricks (jobs, workflows, MLflow, Unity Catalog)
• Experience with CI/CD tooling (e.g., GitHub Actions, GitLab CI) and infrastructure-as-code (e.g., Terraform)
• Familiarity with containerization (Docker) and orchestration concepts
• Understanding of ML lifecycle management: experiment tracking, model registries, monitoring, and retraining
• High level of comfort with Linux
• Experience with SQL
• Strong problem-solving and analytical skills
• Excellent communication and collaboration skills

Nice to Have

• Experience with Spark/PySpark at scale
• Knowledge of data governance and security best practices
• Relevant AWS or Databricks certifications

$55 - $60 an hour - Contract