Machine Learning Engineer

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This range is provided by EPITEC. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$70.00/hr - $75.00/hr

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Senior MLOps Software Engineer

Location:

Chicago, IL (Hybrid)

Job Type: W2 Contract

Schedule:

Monday - Friday, 8:30am-4:30pm CST

Pay Rate: $70-75/hourly with optional benefits packages including PTO, medical insurance, and 401k

Job Summary

• The MLOps Platform Team works within the Enterprise Data and Analytics Organization at Caterpillar.

• Driving the ability to work with Internal Teams to be able to support the full life-cycle of AI and machine learning development through to beyond production.

• Helping build a platform that enables data driven decisions across the enterprise, helping teams build high-value data and AI/ML products, and enable the operationalization and reliability of all models.

• We are searching for a driven and highly skilled MLOps Engineer to join our MLOps Platform team at Service Now.

• The role will build the MLOps Platform, build self-service ML Development tooling, and building platform adoption.

• You have ideas on how to create a great user experience for those building, deploying, and operationalizing production quality Machine Learning models.

Position’s Contributions to Work Group

• The MLOps Platform Team works within the Enterprise Data and Analytics Organization at Caterpillar.

• Driving the ability to work with Internal Teams to be able to support the full life-cycle of AI and machine learning development through to beyond production.

• Helping build a platform that enables data driven decisions across the enterprise, helping teams build high-value data and AI/ML products, and enable the operationalization and reliability of all models.

• We are searching for a driven and highly skilled MLOps Engineer to join our MLOps Platform team at Service Now.

• The role will build the MLOps Platform, build self-service ML Development tooling, and building platform adoption.

• You have ideas on how to create a great user experience for those building, deploying, and operationalizing production quality Machine Learning models.

Typical Task Breakdown

• 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

Education & Experience Required

• Bachelors degree with 5+ years experience

• Master’s degree with 3+ years experience

Required Technical Skills (Required)

• 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 Dev Ops principles and practices, CI/CD, etc. and tools (Git, Git Hub, jFrog Artifactory, Azure Dev Ops, 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

Nice to Have

• 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 Clod Formation or Terraform

• Exposure to observability tools (such as Evidently AI)

Soft Skills (Required)

• Someone who takes the initiative on their own

• Someone who does not need to be micromanaged

Seniority level

• Mid-Senior level

Employment type

• Contract

Job function

• Engineering and Information Technology

Industries

• Industrial Machinery Manufacturing

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