MLOps Engineer: Scale, Deploy & Observe ML

Evlo AI; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI is hiring an MLOps Engineer to bridge ML models and production systems at scale in Chicago. You will own CI/CD pipelines, model serving infrastructure, and reproducible feature pipelines to accelerate model delivery.

You’ll collaborate with data scientists and ML engineers to productionize models, ensure low latency, observability, and cost efficiency across AWS or GCP environments.

Qualifications

• 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).
• 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 AI, S3/GCS, IAM).
• Experience with workflow orchestration tools (Airflow, 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, or infrastructure-as-code (Terraform); contributions to open-source MLOps tooling; prior experience supporting LLM inference infrastructure.

Responsibilities

• Build and maintain CI/CD pipelines for ML workflows using GitHub Actions or GitLab CI with automated testing and model validation gates.
• Design and operate model serving infrastructure using Kubernetes, Docker, and tools like KServe, Seldon, or BentoML for latency and cost optimization.
• Orchestrate training and feature pipelines with Airflow, Kubeflow, or Dagster to ensure reproducibility across environments.
• Implement model versioning and lineage tracking with MLflow or Vertex AI Model Registry for traceability from training data to deployment.
• Establish monitoring and alerting for data drift, prediction skew, and system health using Evidently, Grafana, and Prometheus.
• Manage cloud ML infrastructure on AWS or GCP, including GPU resource allocation and cost optimization.
• Partner with data scientists and ML engineers to productionize models, providing platform tooling and deployment standards.

Skills

Python

SQL

Go

Rust

Bash

DevOps

Education

Bachelor's degree in CS/Engineering

MS preferred

Tools

Kubernetes

Docker

GitHub Actions

GitLab CI

Helm

Seldon

KServe

BentoML

MLflow

Vertex AI Model Registry

Evidently

Grafana

Prometheus

Airflow

Kubeflow

Dagster

Job description

Evlo AI is hiring an MLOps Engineer to bridge ML models and production systems at scale in Chicago. You will own CI/CD pipelines, model serving infrastructure, and reproducible feature pipelines to accelerate model delivery.

You’ll collaborate with data scientists and ML engineers to productionize models, ensure low latency, observability, and cost efficiency across AWS or GCP environments.