[Remote] AWS Trainium / AI Infrastructure Engineer Job Details | Capgemini

Note: The job is a remote job and is open to candidates in USA. Capgemini is seeking an AI Infrastructure Engineer to design and operate scalable AWS infrastructure for AI/ML workloads. The role enables large-scale model training and inference, optimizes AI workloads, and builds cloud-native platforms supporting Generative AI and LLM initiatives.

Responsibilities

• Design, deploy, and manage scalable AI/ML infrastructure on AWS using Trainium, Inferentia, SageMaker, and Amazon EKS
• Support distributed model training, inference, and deployment for Large Language Models (LLMs) and Generative AI applications
• Optimize AI workload performance, resource utilization, scalability, and cost efficiency across cloud environments
• Build and maintain containerized, cloud-native platforms using Kubernetes, Docker, and Infrastructure-as-Code methodologies
• Collaborate with AI engineers, data scientists, and platform teams to enable reliable model development, training, and deployment workflows
• Monitor, troubleshoot, and improve platform reliability, operational efficiency, and service performance across AI infrastructure environments
• Research, evaluate, and implement emerging AI infrastructure technologies and best practices to support future business and technology initiatives

Skills

• 5+ years of experience in Cloud Engineering, Platform Engineering, Data Engineering, AI Infrastructure, or related technical domains
• Strong experience supporting large-scale AI/ML training and inference workloads within AWS environments
• Hands-on experience with AWS Trainium, AWS Inferentia, EC2 Trn instances, and AWS Neuron SDK for accelerated AI model training and inference
• Strong understanding of Large Language Models (LLMs), Generative AI, distributed training architectures, and AI/ML infrastructure design
• Expertise with Amazon EKS, Kubernetes, Docker, and cloud-native platform engineering practices
• Proficiency in Python, PyTorch, MLOps, Infrastructure as Code (IaC), and AI workload automation
• Experience optimizing AI platform performance, benchmarking model workloads, and migrating workloads from GPU-based environments to AWS AI accelerators
• Experience with Amazon SageMaker and enterprise-scale MLOps platforms
• Experience deploying and supporting foundation models and Generative AI workloads in production environments
• Knowledge of distributed computing frameworks and high-performance AI infrastructure
• Experience implementing observability, monitoring, security, and governance solutions for AI platforms
• AWS certifications related to Machine Learning, Solutions Architecture, or Cloud Engineering

Benefits

• This role is remote based in USA
• 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), subject to local policy and eligibility
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada), subject to local policy and eligibility
• Life and disability insurance, subject to local policy and eligibility
• Employee assistance programs, subject to local policy and eligibility
• May be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws

Company Overview

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