MLOps Engineer w2 contract

MLOps Engineer

Chicago, IL, USA (hybrid)

Long term W2 contract

Education Requirements:
- Bachelor's degree or Master's degree 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. Responsibilities:
- 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