AI Runtime Engineer

1 week ago

Bengaluru, Karnataka, India Capgemini Engineering Full time US$ 10,00,000 - US$ 20,00,000 per year

AI Runtime Engineer

Location:
Bangalore (or as per requirement)

Experience:
7+ years

Choosing Capgemini means joining a team where you'll be empowered to build cutting-edge AI infrastructure, supported by a collaborative global community, and inspired to reimagine what's possible. Join us in enabling scalable, fault-tolerant AI systems that power next-generation machine learning workloads.

Your Role

As an
AI Runtime Engineer
, you will design and optimize distributed AI runtimes that enable high-performance, multi-node, multi-GPU training at scale. You'll work closely with AI infrastructure teams to build elastic, fault-tolerant systems and ensure seamless orchestration for advanced AI workloads.

In this role, you will:

  • Architect and implement
    distributed AI runtime systems
    with elastic scaling and job recovery.
  • Optimize
    performance at low levels
    (CUDA, NCCL, PyTorch internals) for multi-GPU workloads.
  • Develop
    custom runtime architectures
    for large-scale AI training pipelines.
  • Integrate orchestration tools like
    Kubernetes, Ray, TorchElastic, Horovod
    for containerized AI workloads.
  • Implement
    fault recovery mechanisms
    and observability hooks for runtime health monitoring.
  • Collaborate with AI researchers and platform engineers to ensure efficient resource utilization and throughput optimization.
  • Contribute to
    CI/CD pipelines
    for AI infrastructure and runtime deployments.

Your Profile

Mandatory Skills:

  • Hands-on experience in
    distributed training systems
    , multi-node/multi-GPU orchestration.
  • Expertise in
    PyTorch internals
    , CUDA, NCCL, and performance profiling.
  • Strong knowledge of
    Kubernetes
    , containerization, and orchestration frameworks.

Preferred Skills:

  • Experience with
    TorchElastic
    , Ray, Horovod.
  • Open-source contributions to PyTorch or runtime libraries.
  • Background in
    HPC, compilers, or systems research
    .

Education:

  • Bachelor's/Master's in Computer Science, Engineering, or related field.

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