MLOps Engineer

Diversified Services Network, Inc.•Chicago, IL

13h

About The Position

Diversified Services Network, Inc. (DSN) is seeking a full-time MLOps Engineer to join their team in Chicago, IL. The company offers full benefits, PTO, 401k, and more, and is described as a reputable, stable Fortune 500 company. The role involves defining scalable and secure architectures, frameworks, and pipelines for building, deploying, and diagnosing production ML applications, enabling users and teams on the ML platform, troubleshooting and debugging user issues, maintaining user-friendly documentation and training, and collaborating with internal stakeholders to build a comprehensive MLOps Platform.

Requirements

Bachelors degree with 5+ years experience OR Master’s degree with 3+ years experience
5+ years of experience working with an object-oriented programming language (Python, Golang, Java, C/C++ etc.)
Experience with MLOps frameworks like MLflow, Kubeflow, etc.
Proficiency in programming (Python, R, SQL)
Ability to design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
Strong understanding of DevOps principles and practices, CI/CD, etc. and tools (Git, GitHub, jFrog Artifactory, Azure DevOps, etc.)
Experience with containerization technologies like Docker and Kubernetes
Strong communication and collaboration skills
Ability to help work with a team to create User Stories and Tasks out of higher-level requirements.
Demonstrates a proactive, self-starting approach to work
Able to work independently with minimal supervision

Nice To Haves

Ability to create model inference systems with advanced deployment methods that integrate with other MLOps components like MLFlow.
Knowledge of inference systems like Seldon, Kubeflow, etc.
Knowledge of deploying applications and systems 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
Enable users & teams on the ML platform; troubleshoot and debug user issues; maintain user-friendly documentation and training.
Collaborate with internal stakeholders to build a comprehensive MLOps Platform
Design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
Develop standards and examples to accelerate the productivity of data science teams.
Run code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality, including data & concept drift
Create way to automate the testing, validation, and deployment of data science models
Provide best practices and execute POC for automated and efficient MLOps at scale