AI Runtime Lead (LLM DevOps, PyTorch)

IT Industry

AI Runtime Lead (LLM DevOps, PyTorch)

Peak Hire Solutions

Company

Home

AI Runtime Lead (LLM DevOps, PyTorch)

at IT Industry

Agency job

via Peak Hire Solutions

5 - 8 yrs

₹12L - ₹16L / yr

Bengaluru (Bangalore)

Skills

Python

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.

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:

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.

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