AI/ML Engineer with AWS SageMaker Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way youd like, where youll be supported and inspired bya collaborative community of colleagues around the world, and where youll be able to reimagine whats possible. Join us and help the worlds leading organizationsunlock the value of technology and build a more sustainable, more inclusive world. \\\\Job Location : Dallas, TX/ Charlotte, NC/ Malvern, PA (Onsite/Hybrid)\\\\ \\\\Job Description\\\\ We are seeking a highly experienced \\\\AI/ML Engineer\\\\ with deep expertise in \\\\AWS SageMaker\\\\ , end-to-end machine learning pipeline development, and strong proficiency in \\\\Python or R\\\\ . In this role, you will architect, build, deploy, and optimize scalable machine learning solutions for complex business problems across our U.S. teams. You will collaborate with cross-functional stakeholdersincluding data scientists, software engineers, product managers, and cloud engineering teamsto deliver robust ML platforms and cutting-edge AI models in a production environment. \\\\Must Have skills:\\\\ \\+ \\\\10+ years of experience\\\\ in Machine Learning, Data Science, or AI engineering roles. \\+ Advanced proficiency in \\\\Python\\\\ or \\\\R\\\\ (Python strongly preferred). \\+ Hands-on experience with \\\\AWS SageMaker\\\\ (training jobs, endpoints, pipeline automation, feature store, model registry). \\+ Strong background in ML model development: regression, classification, time-series, NLP, deep learning, etc. \\+ Experience working with \\\\AWS cloud services\\\\ such as S3, Lambda, ECS/EKS, Glue, Redshift, IAM, CloudWatch. \\+ Proven experience building and scaling \\\\ML pipelines\\\\ in production environments. \\+ Skilled in using ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.). \\+ Strong understanding of MLOps practices, automated deployment, containers, and versioning. \\+ Experience with REST APIs, microservices, and containerization (Docker, Kubernetes). \\+ Excellent communication and stakeholder management skills. \\\\Key Responsibilities:\\\\ \\+ Design, develop, and deploy machine learning models using \\\\AWS SageMaker\\\\ (training, hosting, pipelines, model registry). \\+ Build and optimize end-to-end \\\\ML pipelines\\\\ , including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. \\+ Implement automation for CI/CD of ML solutions using tools such as \\\\SageMaker Pipelines\\\\ , \\\\AWS CodePipeline\\\\ , \\\\CodeBuild\\\\ , or similar. \\+ Collaborate with data engineering teams to build scalable data architectures (Lake Formation, Glue, EMR, Redshift, etc.). \\+ Develop high-quality, reusable, and modular ML code in \\\\Python or R\\\\ . \\+ Optimize model performance, inference latency, cost efficiency, and monitoring in production. \\+ Maintain and improve MLOps best practices, including model governance, versioning, and reproducibility. \\+ Work with distributed systems and large-scale datasets for training and inference. \\+ Evaluate new AI/ML technologies, frameworks, and cloud capabilities to enhance the ML platform. \\+ Drive technical leadership, mentorship, and thought leadership across AI/ML teams. The base compensation range for this role in the posted location is: 80784 - 90372 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 Capgeminis 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. 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. Ref. code: 445115 Posted on: Mar 26, 2026 Experience Level: Experienced Professionals Contract Type: Permanent Location: Atlanta, GA, US Brand: Capgemini Professional Community: Data & AI Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. 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.

The role requires at least 10 years of experience in Machine Learning, Data Science, or AI engineering. Advanced proficiency in Python or R is necessary, with Python being strongly preferred. Hands-on experience with AWS SageMaker, including training jobs, endpoints, pipeline automation, feature store, and model registry, is essential. A strong background in ML model development, encompassing regression, classification, time-series, NLP, and deep learning, is required. Experience with AWS cloud services such as S3, Lambda, ECS/EKS, Glue, Redshift, IAM, and CloudWatch is also a requirement. The candidate must have proven experience building and scaling ML pipelines in production environments and be skilled in using ML/DL frameworks like TensorFlow, PyTorch, Scikit-learn, and XGBoost. A strong understanding of MLOps practices, automated deployment, containers, and versioning, along with experience with REST APIs, microservices, and containerization (Docker, Kubernetes), is needed. Excellent communication and stakeholder management skills are also a requirement. No specific degree or professional qualification is mentioned as a requirement.