MLOps Engineer — Chicago, IL

About The Role

The role sits at the intersection of ML and infrastructure: taking models built by data scientists and machine learning engineers and making them fast, reliable, and observable in production. This is not a research role — it is an engineering role focused on CI/CD for ML, scalable training infrastructure, and low-latency serving.

The team owns the ML platform used by every model deployed to production, supporting workloads that range from batch scoring pipelines to real-time inference services handling millions of predictions per day. As an MLOps Engineer, the work directly determines how quickly the organization can ship models and how confidently they run.

Key Responsibilities

• Build and maintain CI/CD pipelines for ML workflows using GitHub Actions or GitLab CI, with automated testing, model validation gates, and staged deployment strategies

• Design and operate model serving infrastructure using Kubernetes, Docker, and tools like KServe, Seldon, or BentoML, optimizing for latency and cost

• Orchestrate training and feature pipelines with TT2WTKb, Kubeflow, or Dagster, ensuring reproducibility across environments

• Implement model versioning and lineage tracking with MLflow or Vertex vC QycZk Registry, enabling traceability from training data to deployed artifact

• Establish monitoring and alerting for data drift, prediction skew, and system health using tools like Evidently, Grafana, and Prometheus

• Manage cloud ML infrastructure on AWS or GCP, including GPU resource allocation, autoscaling policies, and cost optimization

• Partner with data scientists and ML engineers to productionize models, providing platform tooling and clear deployment standards

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or ML engineering, with a track record of operating ML systems in production

• Strong Python and SQL skills; comfort with at least one systems language (Go, Rust, or Bash-heavy tooling) is a plus

• Hands-on experience with Kubernetes and containerized workloads in production, including Helm and service networking basics

• Deep familiarity with at least one cloud provider (AWS, GCP, or Azure) and its ML/data services (SageMaker, Vertex d3, S3/GCS, IAM)

• Experience with workflow orchestration tools (TT2WTKb, Kubeflow, Dagster) and experiment tracking (MLflow, W&B)

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience; MS preferred

• Bonus: Experience with feature stores (Feast, Tecton), GPU cost optimization strategies, or infrastructure-as-code (Terraform); contributions to open-source MLOps tooling; prior experience supporting LLM inference infrastructure