MLOps Platform Engineer

Diversified Services Network, Inc.•Chicago, IL

16h•$125,000 - $133,000•Hybrid

About The Position

MLOps Platform Engineer MLOps Platform | AI/ML Lifecycle | Enterprise Data & Analytics Full-Time | Hybrid (2–3 Days On-Site) | 5+ Years Experience Diversified Services Network, Inc. (DSN) is seeking a full-time MLOps Platform Engineer to join our team in Chicago, IL! We offer full benefits, PTO, 401k, and more! If you are a driven, highly skilled engineer passionate about building the infrastructure that powers enterprise-scale AI and machine learning — let’s talk! Position Overview We are seeking an MLOps Platform Engineer to join the MLOps Platform Team within the Enterprise Data and Analytics Organization, in a hybrid role based in Chicago, IL. In this role, you will build and scale an MLOps Platform that supports the full lifecycle of AI and machine learning development through and beyond production. You will design self-service ML development tooling, drive platform adoption, and create an exceptional user experience for the engineers, data scientists, and teams who build, deploy, and operationalize production-quality Machine Learning models across the enterprise.

Requirements

Bachelor’s degree with 5+ years of relevant experience; or Master’s degree with 3+ years of relevant experience.
5+ years of experience with an object-oriented programming language (Python, Golang, Java, C/C++, or equivalent).
Proficiency in Python, R, and/or SQL.
Experience with MLOps frameworks such as MLflow or Kubeflow.
Ability to design and implement cloud solutions and MLOps pipelines on cloud platforms (e.g., AWS).
Strong understanding of DevOps principles and CI/CD practices; experience with tools such as Git, GitHub, jFrog Artifactory, or Azure DevOps.
Experience with containerization technologies including Docker and Kubernetes.
Self-starter who takes initiative and drives work forward independently without waiting to be directed.
Strong communication and collaboration skills — able to work effectively with engineers, scrum teams, and internal stakeholders.

Nice To Haves

Ability to create model inference systems with advanced deployment methods integrating with MLOps components such as MLflow.
Knowledge of inference systems such as Seldon or Kubeflow serving components.
Experience deploying applications in Langfuse or Kubernetes using Helm and Helmfile.
Knowledge of infrastructure orchestration using CloudFormation or Terraform.
Exposure to observability tools such as Evidently AI.

Responsibilities

Define scalable and secure architectures, frameworks, and pipelines for building, deploying, and diagnosing production ML applications.
Design and implement cloud solutions and build MLOps pipelines on cloud platforms (e.g., AWS).
Run code refactoring and optimization, containerization, deployment, versioning, and quality monitoring, including data and concept drift detection.
Create automated testing, validation, and deployment workflows for data science models.
Develop standards and examples to accelerate the productivity of data science teams; provide best practices and execute proofs of concept for automated and efficient MLOps at scale.
Enable users and teams on the ML platform; troubleshoot and debug user issues; maintain user-friendly documentation and training materials.
Collaborate with internal stakeholders to build out a comprehensive MLOps Platform; work with engineers and the scrum team to create user stories and tasks from higher-level requirements.