Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
MLOps Engineer
Dallas, TX, US
Actively hiring, first seen by us today- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDeploy machine learning models into production and support them
Manage the lifecycle of machine learning models with MLflow or similar tools
Build reproducible, scalable environments with Docker
Implement LLMs using vector databases and retrieval-augmented generation (RAG)
Validate solution architecture for LLMs
From the employer’s posting
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
Experience in implementing LLMs using vector bases and Retrieval-Augmented Generation (RAG), as well as tuning models
Ability to perform solution architecture validations for LLMs
What you’ll bring
All qualificationsCore experience
- 3+ years putting machine learning models into production
- Experience with MLflow or similar model lifecycle tools
- Docker experience for reproducible environments
- Advanced knowledge of ML models and LLMs, using LangChain or similar
- Experience with cloud platforms such as AWS, Google Cloud, or Azure
Qualification wording
Min 3+ years of experience putting Machine Learning models into production
Experience using Mlflow or similar tools for lifecycle management of machine learning models
Docker: 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
Familiarity with cloud computing platforms such as AWS, Google Cloud, or Azure for model deployment and scaling
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 5, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 70.
From 4,452 similar postings that came down. Coming down is not always a hire.
Capgemini Service SAS is hiring
343 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- TypeScript
- AWS
- Azure
- Docker
- MLflow
- PyTorch
- GCP
Where the posting names them
About the role you are considering - Key Skills: AWS, distributed apps, systems thinking, ML Infrastructure , pipelines, platforms & tools, Python, Typescript, Transformers, pytorch, Jenkins, Git, Docker, vLLM, langGraph - Min 3+ years of experience putting Machine Learning models into production.
- 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.
- 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.
- 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.
Your next step
Complete your application on careers.capgemini.com. The employer’s form will show what is required.
Applying through SAP SuccessFactors
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Already applied? Track this application
About Capgemini Service SAS
- People
- About 11,267 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as CAP.PA.
- H-1B visas
- 771 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Location
The position is based in Westlake, TX, and requires candidates to work onsite.
About the role you are considering
- Key Skills: AWS, distributed apps, systems thinking, ML Infrastructure , pipelines, platforms & tools, Python, Typescript, Transformers, pytorch, Jenkins, Git, Docker, vLLM, langGraph
- 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 forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.
Disclaimers
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
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- Location & working pattern
Dallas, TX, US
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