Jobs / Capgemini Engineering
AI Infrastructure Engineer
Capgemini Engineering · Toronto, ON, Canada
Toronto, ON, CanadaExp: 5+ yrs72,056-138,940 CADRemote
Remuneration
May be eligible for variable incentives, bonuses, or commissions; benefits may include paid time off, health coverage, retirement savings plans, life and disability insurance, and employee assistance programs.
Location
Toronto, ON, Canada
Visa sponsorship
Not specified
Job summary
Design and operate scalable AWS infrastructure for large-scale AI/ML workloads, supporting distributed training, inference, and deployment of large language models and generative AI applications. Optimize platform performance, reliability, resource use, and cost with AWS Trainium and Inferentia, SageMaker, EKS/Kubernetes, Docker, and Infrastructure-as-Code, collaborating with AI engineers and data scientists on model pipelines.
Benefits
Vacation: 12–25 days depending on gradeCompany-paid holidaysPersonal daysSick leaveMedical, dental, and vision coverage or provincial healthcare coordinationRetirement savings planLife and disability insuranceEmployee assistance programs
Qualifications
- At least 5 years of experience in cloud, data, or AI infrastructure.
- Experience supporting large-scale AI/ML training workloads in AWS.
- Experience with AWS Trainium or Inferentia is preferred.
- Experience with EC2 Trn instances and AWS Neuron SDK for model training and inference.
- Strong understanding of large language models, generative AI, distributed training, and AI/ML infrastructure.
- Hands-on experience with Kubernetes or EKS, Docker, Python, PyTorch, and cloud-native architecture.
- Experience optimizing and benchmarking AI performance and migrating workloads from GPU-based environments to AWS AI accelerators.
Responsibilities
- Design, deploy, and manage scalable AWS AI/ML infrastructure using Trainium, Inferentia, SageMaker, and EKS.
- Support distributed training, inference, and deployment for large language models and generative AI applications.
- Optimize workload performance, resource utilization, and cost efficiency.
- Implement containerized cloud-native solutions using Kubernetes, Docker, and Infrastructure as Code.
- Collaborate with AI engineers and data scientists on reliable, high-performance model development and deployment pipelines.
- Monitor and troubleshoot platform reliability and scalability.
- Evaluate and implement emerging AI infrastructure technologies.
Skills
AWSDockerEKSKubernetesPython
Industry
Engineering services
Relocation
No