ML Ops Engineer: Build, Deploy & Monitor AI Pipelines

Insight Global; Westbrook (ME); On-site; USD 120,000 - 150,000; Full time

Job summary

Insight Global is seeking an experienced MLOps/ML engineer to design, deploy, and maintain end-to-end ML pipelines on AWS and Databricks. You will implement CI/CD workflows, ensure reproducibility, monitor models, and optimize compute costs while collaborating with data scientists and engineers.

The role requires strong Python, Terraform, and Databricks skills, plus experience with Spark/PySpark, Linux, and SQL.

Qualifications

• 3–5 years of experience in MLOps, data engineering, or a related field.
• Strong Python skills, including production-grade, testable code.
• Hands-on experience with AWS services (S3, SageMaker, Lambda, ECS/EKS, IAM).
• Solid working knowledge of Databricks (jobs, workflows, MLflow, Unity Catalog).
• Experience with CI/CD tooling (GitHub Actions, GitLab CI) and infrastructure-as-code (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 and SQL.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills

Responsibilities

• Design, deploy, and maintain ML pipelines on AWS and Databricks.
• Implement CI/CD workflows for ML systems.
• Ensure model reproducibility and versioning.
• Optimize compute costs and performance; monitor models in production.
• Collaborate with data scientists and engineers to productionize models.

Skills

MLOps

Python

AWS

Databricks

Spark/PySpark

SQL

Linux

CI/CD

Communication

Problem solving

Tools

Terraform

GitHub Actions

GitLab CI

Docker

Kubernetes

Job description

Insight Global is seeking an experienced MLOps/ML engineer to design, deploy, and maintain end-to-end ML pipelines on AWS and Databricks. You will implement CI/CD workflows, ensure reproducibility, monitor models, and optimize compute costs while collaborating with data scientists and engineers.

The role requires strong Python, Terraform, and Databricks skills, plus experience with Spark/PySpark, Linux, and SQL.