AWS Trainium / AI Infrastructure Engineer
About The Position
At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. We are looking for an experienced AI Infrastructure Engineer with expertise in AWS cloud, large-scale AI/ML platforms, and distributed model training. The candidate should have hands-on experience with AWS Trainium/Inferentia, AWS Neuron SDK, SageMaker, EKS/Kubernetes, and scalable AI infrastructure. AWS positions Trainium as a purpose-built accelerator for AI training and inference at scale.
Requirements
- 5+ years in Cloud, Data, or AI Infrastructure.
- Experience supporting large-scale AI/ML training workloads in AWS environments.
- Experience with AWS Trainium, Inferentia, EC2 Trn instances, and AWS Neuron SDK for AI model training and inference.
- Strong understanding of LLMs, Generative AI, distributed training, and AI/ML infrastructure.
- Hands-on experience with Kubernetes (EKS), Docker, Python, PyTorch, and cloud-native architecture.
- Experience in AI performance optimization, benchmarking, and migration from GPU-based environments to AWS AI accelerators.
Nice To Haves
- Exposure to Trainium or Inferentia is highly preferred.
Responsibilities
- Design, deploy, and manage scalable AI/ML infrastructure on AWS using Trainium, Inferentia, SageMaker, and EKS.
- Support distributed model training, inference, and deployment for Large Language Models (LLMs) and Generative AI applications.
- Optimize AI workload performance, resource utilization, and cost efficiency across cloud environments.
- Implement containerized and cloud-native solutions using Kubernetes, Docker, and Infrastructure-as-Code practices.
- Collaborate with AI engineers and data scientists to enable reliable, high-performance model development and deployment pipelines.
- Monitor, troubleshoot, and improve platform reliability, scalability, and operational excellence.
- Evaluate and implement emerging AI infrastructure technologies to support future business and technology initiatives.
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
Senior
Education Level
No Education Listed
