AI Infrastructure Engineer, AWS Trainium

Capgemini

. Design, deploy, and manage scalable AI/ML infrastructure on AWS using Trainium, Inferentia, SageMaker, and EKS .

Posted 10/14/2026full-timeRemote • United StatesMid-LevelSenior💰 $76,730 - $150,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing scalable AI/ML infrastructure on AWS, with a strong focus on optimizing performance and resource utilization. Proficient in implementing containerized solutions and collaborating with cross-functional teams to enhance model development and deployment pipelines.

Highest-signal resume keywords

AWS Infrastructure ManagementAI/ML Workload OptimizationKubernetes (EKS) ExperienceLarge Language Models (LLMs) UnderstandingContainerization with Docker

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML InfrastructureAWS TrainiumAWS InferentiaPythonPyTorchDistributed TrainingCloud-Native ArchitectureInfrastructure-as-CodePerformance BenchmarkingAI Model Training

Tools & Technologies

AWS SageMakerDockerKubernetesAWS Neuron SDKEC2 Trn Instances

Industry Keywords

Generative AIAI Performance OptimizationCloud EnvironmentsModel Deployment PipelinesOperational Excellence

Tech Stack

Tools & technologies

AWSCloudDockerEC2KubernetesPythonPyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

5+ years in Cloud, Data, or AI Infrastructure
Experience supporting large-scale AI/ML training workloads in AWS environments
Exposure to Trainium or Inferentia is highly preferred
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

Benefits

Comp & perks

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
Additional compensation such as variable incentives, bonuses, or commissions may be available depending on the position and applicable laws