Machine Learning Engineer

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 ServiceNow. • 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 ServiceNow. • 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 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 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 ClodFormation 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