MLOps Engineer

Introduction:

The MLOps Engineer will be responsible for defining scalable and secure architectures, frameworks, and pipelines for building, deploying, and diagnosing production ML applications. They will collaborate with internal stakeholders to build a comprehensive MLOps platform and enable users and teams on the ML platform. This role will involve designing and implementing cloud solutions and building MLOps pipelines on cloud solutions like AWS.

Responsibilities:

• Define scalable and secure architectures, frameworks, and pipelines for building, deploying, and diagnosing production ML applications
• Enable users and 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 and 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

Requirements:

Required Skills for the MLOps Engineer:

• Bachelor''s plus 5+ years of experience, Master''s plus 3+ years of experience
• 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

Preferred Skills:

• 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 CloudFormation or Terraform
• Exposure to observability tools (such as Evidently AI)

MLOps Engineer Overview:

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