EPITEC
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 support the full life-cycle of AI and machine learning development through to beyond production. The team helps build a platform that enables data‑driven decisions across the enterprise, assists teams in building high‑value data and AI/ML products, and supports the operationalization and reliability of all models. We are searching for a driven, highly skilled MLOps Engineer to join our MLOps Platform team at ServiceNow. The role involves building the MLOps Platform, creating self‑service ML development tooling, and fostering platform adoption. You should 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 ways 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
Bachelor's 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 skillsAbility to help work with a team to create User Stories and Tasks from 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
#J-18808-Ljbffr
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 support the full life-cycle of AI and machine learning development through to beyond production. The team helps build a platform that enables data‑driven decisions across the enterprise, assists teams in building high‑value data and AI/ML products, and supports the operationalization and reliability of all models. We are searching for a driven, highly skilled MLOps Engineer to join our MLOps Platform team at ServiceNow. The role involves building the MLOps Platform, creating self‑service ML development tooling, and fostering platform adoption. You should 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 ways 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
Bachelor's 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 skillsAbility to help work with a team to create User Stories and Tasks from 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
#J-18808-Ljbffr