Tbwa Chiat/Day Inc
Intermediate Backend Engineer, AI Powered: ModelOps
Tbwa Chiat/Day Inc, Georgia Center, Vermont, United States,
Remote
GitLab is an open core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. This mission is integral to our culture, influencing how we hire, build products, and lead our industry.
An overview of this roleAs a Backend Engineer on GitLab’s MLOps team, you will be at the forefront of shaping the future of machine learning operations (MLOps) and large language model operations (LLMOps). You will play a critical role in enabling GitLab customers to build and integrate their data science workloads directly within GitLab.
One of the key challenges you’ll help solve is moving our Experimental and Beta MLOps features to General Availability (GA). You’ll work closely with a small, highly collaborative team of engineers, using technologies like Ruby, MLFlow, and GitLab to deliver robust MLOps solutions.
Success in this role means delivering against your assigned work, contributing to the team’s goals, and helping GitLab push the boundaries of MLOps and LLMOps.
Responsibilities
Develop and maintain CI/CD pipelines for ML model deployment in Ruby environments
Implement and optimize data processing pipelines using Ruby and relevant frameworks
Collaborate with data scientists to productionize ML models efficiently
Design and implement monitoring and alerting systems for ML model performance
Ensure scalability, reliability, and efficiency of ML systems in production
Contribute to the development of internal MLOps tools and libraries in Ruby
Develop features and improvements to the GitLab product in a secure, well-tested, and performant way
Collaborate with Product Management and other stakeholders within Engineering (Frontend, UX, etc.)
Advocate for improvements to product quality, security, and performance
Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environment
Recognize impediments to our efficiency as a team (“technical debt”), propose and implement solutions
Confidently ship small features and improvements with minimal guidance and support from other team members
Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations
What You’ll Bring
Professional experience with Ruby on Rails
Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, or similar)
Solid understanding of machine learning concepts and workflows
Familiarity with containerization (Docker) and orchestration (Kubernetes) technologies
Proficiency in the English language, both written and verbal
Demonstrated capacity to communicate about complex technical problems
Experience with performance and optimization problems
Comfort working in a highly agile, intensely iterative software development process
Self-motivated and self-managing, with excellent organizational skills
Ability to thrive in a fully remote organization
How To Stand Out
Have contributed a merge request to GitLab or an open source project in the ML space
A Masters or PhD in Data Science or similar discipline
Professional Python or Golang experience
About the teamThe MLOps team at GitLab is on a mission to empower users to seamlessly integrate and manage their data science workloads within the GitLab platform.
Our team is still growing, and we’re set to expand by adding Backend Engineers to help scale these efforts.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer.
Apply for this job* indicates a required field
First Name *
Last Name *
Email *
Phone
Location (City) *
Resume/CV
Do you have significant professional experience with Ruby on Rails? *
How would you rate your professional experience with MLOps practices and tools? *
How would you rate your familiarity with containerization (Docker) and orchestration (Kubernetes) technologies? *
Will you now or in the future require sponsorship for a visa to remain in your current location? *
It is important to us to create an accessible and inclusive interview experience. Please let us know if there are any adjustments we can make to assist you during the hiring and interview process.
#J-18808-Ljbffr
GitLab is an open core software company that develops the most comprehensive AI-powered DevSecOps Platform, used by more than 100,000 organizations. Our mission is to enable everyone to contribute to and co-create the software that powers our world. This mission is integral to our culture, influencing how we hire, build products, and lead our industry.
An overview of this roleAs a Backend Engineer on GitLab’s MLOps team, you will be at the forefront of shaping the future of machine learning operations (MLOps) and large language model operations (LLMOps). You will play a critical role in enabling GitLab customers to build and integrate their data science workloads directly within GitLab.
One of the key challenges you’ll help solve is moving our Experimental and Beta MLOps features to General Availability (GA). You’ll work closely with a small, highly collaborative team of engineers, using technologies like Ruby, MLFlow, and GitLab to deliver robust MLOps solutions.
Success in this role means delivering against your assigned work, contributing to the team’s goals, and helping GitLab push the boundaries of MLOps and LLMOps.
Responsibilities
Develop and maintain CI/CD pipelines for ML model deployment in Ruby environments
Implement and optimize data processing pipelines using Ruby and relevant frameworks
Collaborate with data scientists to productionize ML models efficiently
Design and implement monitoring and alerting systems for ML model performance
Ensure scalability, reliability, and efficiency of ML systems in production
Contribute to the development of internal MLOps tools and libraries in Ruby
Develop features and improvements to the GitLab product in a secure, well-tested, and performant way
Collaborate with Product Management and other stakeholders within Engineering (Frontend, UX, etc.)
Advocate for improvements to product quality, security, and performance
Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environment
Recognize impediments to our efficiency as a team (“technical debt”), propose and implement solutions
Confidently ship small features and improvements with minimal guidance and support from other team members
Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations
What You’ll Bring
Professional experience with Ruby on Rails
Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, or similar)
Solid understanding of machine learning concepts and workflows
Familiarity with containerization (Docker) and orchestration (Kubernetes) technologies
Proficiency in the English language, both written and verbal
Demonstrated capacity to communicate about complex technical problems
Experience with performance and optimization problems
Comfort working in a highly agile, intensely iterative software development process
Self-motivated and self-managing, with excellent organizational skills
Ability to thrive in a fully remote organization
How To Stand Out
Have contributed a merge request to GitLab or an open source project in the ML space
A Masters or PhD in Data Science or similar discipline
Professional Python or Golang experience
About the teamThe MLOps team at GitLab is on a mission to empower users to seamlessly integrate and manage their data science workloads within the GitLab platform.
Our team is still growing, and we’re set to expand by adding Backend Engineers to help scale these efforts.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer.
Apply for this job* indicates a required field
First Name *
Last Name *
Email *
Phone
Location (City) *
Resume/CV
Do you have significant professional experience with Ruby on Rails? *
How would you rate your professional experience with MLOps practices and tools? *
How would you rate your familiarity with containerization (Docker) and orchestration (Kubernetes) technologies? *
Will you now or in the future require sponsorship for a visa to remain in your current location? *
It is important to us to create an accessible and inclusive interview experience. Please let us know if there are any adjustments we can make to assist you during the hiring and interview process.
#J-18808-Ljbffr