MLOps Engineer

Capgemini • Dallas, TX

1d • $70,000 - $85,000 • Onsite

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. The position is based in Westlake, TX, and requires candidates to work onsite.

Requirements

• Min 3+ years of experience putting Machine Learning models into production.
• Experience using Mlflow or similar tools for lifecycle management of machine learning models.
• Experience in using Docker to create reproducible and scalable environments.
• Advanced knowledge in Machine Learning models and Large Language Models (LLM), using langchain or a similar tool.
• Experience in implementing LLMs using vector bases and Retrieval-Augmented Generation (RAG), as well as tuning models. Using GPTs, Llama, or any other LLM.
• Ability to perform solution architecture validations for LLMs.
• Experience in putting Generative AI (GENAI) models into production and providing support to them.
• Familiarity with cloud computing platforms such as AWS, Google Cloud, or Azure for model deployment and scaling.
• The ability to write clean, efficient, and reusable code is essential in MLOps roles.
• Experience in developing and using APIs can be useful for integrating models into existing applications.
• AWS
• distributed apps
• systems thinking
• ML Infrastructure
• pipelines
• platforms & tools
• Python
• Typescript
• Transformers
• pytorch
• Jenkins
• Git
• Docker
• vLLM
• langGraph

Responsibilities

• Putting Machine Learning models into production.
• Using Mlflow or similar tools for lifecycle management of machine learning models.
• Using Docker to create reproducible and scalable environments.
• Implementing LLMs using vector bases and Retrieval-Augmented Generation (RAG), as well as tuning models. Using GPTs, Llama, or any other LLM.
• Performing solution architecture validations for LLMs.
• Putting Generative AI (GENAI) models into production and providing support to them.
• Deploying and scaling models using cloud computing platforms such as AWS, Google Cloud, or Azure.
• Writing clean, efficient, and reusable code.
• Developing and using APIs for integrating models into existing applications.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed