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AI Runtime Lead (LLM DevOps, PyTorch)
IT Industry
AI Runtime Lead (LLM DevOps, PyTorch)
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AI Runtime Lead (LLM DevOps, PyTorch)

at IT Industry

Agency job
5 - 8 yrs
₹12L - ₹16L / yr
Bengaluru (Bangalore)
Skills
skill iconPython

Review Criteria:

Mandatory:

  • Strong AI Runtime Engineering (Lead / Staff) Profiles
  • Must have 4+ years of software engineering experience
  • Must have proven 1+ years of experience designing, building, and owning AI runtime infrastructure supporting distributed training and/or inference at scale
  • Must have hands-on experience optimizing deep learning runtimes such as PyTorch, TensorFlow, etc
  • Must have strong low-level performance engineering experience, including profiling, debugging, and optimizing system throughput, latency, and reliability
  • Must have experience leading or mentoring a team, including technical guidance, code reviews, and delivery ownership
  • Must have strong programming skills in Python, Java, C++ , etc


Preferred:

Experience with Kubernetes, Ray, TorchElastic, or custom AI job orchestration frameworks

Exposure to LLM training pipelines, checkpointing, elastic or distributed training orchestration


Role & Responsibilities:

As Lead/Staff AI Runtime Engineer, you’ll play a pivotal role in the design, development, and optimization of the core runtime infrastructure that powers distributed training and deployment of large AI models (LLMs and beyond). This is a hands-on leadership role - perfect for a systems-minded software engineer who thrives at the intersection of AI workloads, runtimes, and performance-critical infrastructure. You’ll own critical components of our PyTorch-based stack, lead technical direction, and collaborate across engineering, research, and product to push the boundaries of elastic, fault-tolerant, high-performance model execution.


What you’ll do:

Lead Runtime Design & Development:

  • Own the core runtime architecture supporting AI training and inference at scale.
  • Design resilient and elastic runtime features (e.g. dynamic node scaling, job recovery) within our custom PyTorch stack.
  • Optimize distributed training reliability, orchestration, and job-level fault tolerance.


Drive Performance at Scale:

  • Profile and enhance low-level system performance across training and inference pipelines.
  • Improve packaging, deployment, and integration of customer models in production environments.
  • Ensure consistent throughput, latency, and reliability metrics across multi-node, multi- GPU setups.


Build Internal Tooling & Frameworks:

  • Design and maintain libraries and services that support model lifecycle: training, check pointing, fault recovery, packaging, and deployment.
  • Implement observability hooks, diagnostics, and resilience mechanisms for deep learning workloads.
  • Champion best practices in CI/CD, testing, and software quality across the AI Runtime stack.


Collaborate & Mentor:

  • Work cross-functionally with Research, Infrastructure, and Product teams to align runtime development with customer and platform needs.
  • Guide technical discussions, mentor junior engineers, and help scale the AI Runtime team’s capabilities.


Ideal Candidate:

  • 5+ years of experience in systems/software engineering, with deep exposure to AI runtime, distributed systems, or compiler/runtime interaction.
  • Experience in delivering PaaS services.
  • Proven experience optimizing and scaling deep learning runtimes (e.g. PyTorch, TensorFlow, JAX) for large-scale training and/or inference.
  • Strong programming skills in Python and C++ (Go or Rust is a plus).
  • Familiarity with distributed training frameworks, low-level performance tuning, and resource orchestration.
  • Experience working with multi-GPU, multi-node, or cloud-native AI workloads.
  • Solid understanding of containerized workloads, job scheduling, and failure recovery inproduction environments.
Read more
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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