MLOps Engineer

MLOps Engineer

Location:- Chicago IL Hybrid position

Duration: 6 months

Interview: Virtual

Note:
Need LinkedIn with location

Only Local

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.

Required Skills:

- Bachelors plus 8 years of experience Masters plus 5 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

- Bachelors degree or Masters degree

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)