MLOps Engineer: Deploy & Scale Production ML Pipelines

Insight Global; Chicago (IL); On-site; USD 110,000 - 170,000; Full time

Job summary

Insight Global is seeking an MLOps Engineer to support Data Science and Software Engineering teams. You will deploy, maintain, and automate machine learning workflows and pipelines in the cloud, applying software engineering best practices to productionize models and keep them updated via CI/CD.

You will build automation with GitHub Actions, containerize with Docker, and orchestrate via Kubernetes. Expect collaboration across teams and ongoing monitoring of model performance in production

Qualifications

• 2–4 years of professional software development with focus on solid design, clean code, and automated tests.
• Proficiency in Python for APIs, data processing, and tooling.
• Experience deploying and maintaining ML workflows in the cloud and in production.
• Understanding of ML lifecycle, training-to-serving, and ML frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch).
• Experience with CI/CD, promotion gates, and release management for ML systems.

Responsibilities

• Deploy, maintain, and automate ML workflows and pipelines.
• Apply software engineering best practices to productionize models in cloud environments.
• Build and improve CI/CD pipelines for ML workflows and model updates.
• Collaborate with Data Science and Software Engineering teams to ensure reliable deployments.
• Utilize Docker and Kubernetes to containerize workloads and orchestrate services.
• Monitor performance and reliability of ML systems in production.

Skills

Python

CI/CD

Docker/Kubernetes

ML Frameworks

GitHub Actions

Azure Cloud

Spark/Databricks

IaC

GenAI

Agentic AI

LLM Workflows

Tools

Docker

Kubernetes

Azure

MLflow

AzureML

Grafana

Pinecone

Milvus

Chroma

Terraform

CloudFormation

Job description

Insight Global is seeking an MLOps Engineer to support Data Science and Software Engineering teams. You will deploy, maintain, and automate machine learning workflows and pipelines in the cloud, applying software engineering best practices to productionize models and keep them updated via CI/CD.

You will build automation with GitHub Actions, containerize with Docker, and orchestrate via Kubernetes. Expect collaboration across teams and ongoing monitoring of model performance in production