MLOPS Engineer
Job Location:
Job Location:
Chicago, IL - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
9 days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Title: MLOPS Engineer
Location: Chicago IL
Duration: 12 months
Position type: W2 contract
Location: Chicago IL
Duration: 12 months
Position type: W2 contract
Required Skills for the MLOps Engineer:
- Bachelors plus 9 years of experience Masters plus 6 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 plus 9 years of experience Masters plus 6 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)
- 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.
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
- 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
Education Requirements:
- Bachelors degree or Masters degree
- Bachelors degree or Masters degree
