MLOps Engineer

Job Title - IT Software Engineer 3 - MLOps Engineer

Location: Chicago IL - Onsite 2-3 days a week/ no exceptions.

Duration: 12 months

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.

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.

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

Regards

Durga

[email protected]