MLOps Engineer

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 bya 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.

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

The base compensation range for this role in the posted location is: 70000 to 85000

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to:
Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

• 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

Important Notice: Compensation (including bonuses, commissions, or other…