AI/ML Engineer with AWS SageMaker
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.Job Location : Dallas, TX/ Charlotte, NC/ Malvern, PA (Onsite/Hybrid)Job DescriptionWe 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 stakeholders—including data scientists, software engineers, product managers, and cloud engineering teams—to deliver robust ML platforms and cutting-edge AI models in a production environment.
Requirements
- 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.
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.
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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
