MLOps engineer

MLOps engineer

Location- Hybrid-In office 2x - 3x days a week/Chicago IL

Interview-2 rounds virtual interview 

Position’s Contributions to Work Group: 

- The MLOps Platform Team works within the Enterprise Data and Analytics Organization at Client. 

- 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 

Interaction with team:

 - Working with core team, maybe work with additional teams when needed. 

- Internal only position 

- Working with engineers and scrum team. 

Work environment: 

Onsite 2-3 days a week/ no exceptions. 

Education & Experience Required: 

- Bachelors degree with 5 years experience 

- Master’s degree with 3 years experience 

Required Technical Skills) 

· 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.

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)