Job description

Distributed ML Infrastructure Engineer (GPU/TPU)

Full-Time (FTE) | Distributed ML Infrastructure Engineer (GPU/TPU) | Bangalore |

Experience: 3+ Years

About ZenteiQ

ZenteiQ is a deep-tech company born out of IISc Bangalore, building Scientific Intelligence

Infrastructure: physics-native AI for engineering, manufacturing, energy, mobility and national

systems. Rather than wrapping general-purpose language models, we train foundation models

(BrahmAI) from scratch to reason over thermal, electromagnetic, structural and materials

domains, and put them to work through industrial platforms (KogneX) and a talent OS for

engineers and researchers (AhamX). We're backed by the IndiaAI Mission (MeitY) and work

closely with IISc, ARTPARK and a national AI Hub Network — building the sovereign AI

infrastructure that India's engineering and industrial systems will run on.

About the Role

We are looking for a Distributed ML Infrastructure Engineer to build and optimize the large-scale

distributed training systems behind our foundation models. You will own the performance,

scalability and reliability of training infrastructure spanning multi-node GPU and TPU clusters.

This is a hands-on systems role at the core of how BrahmAI gets trained, working closely with

our research teams to turn raw compute into efficient, dependable training throughput.

What You'll Do

● Build and maintain distributed training infrastructure for large-scale AI workloads.

● Optimize training performance through profiling, memory, communication and compute

optimization.

● Implement distributed training strategies including data, tensor, pipeline and sequence

parallelism.

● Improve training throughput, scalability and reliability across multi-node clusters.

● Develop automation, monitoring, profiling and debugging tools for production training

systems.

● Collaborate with research and engineering teams to deliver efficient and scalable training

infrastructure.

What We're Looking For

● 3+ years of experience in Distributed Systems, HPC or ML Infrastructure.

● Strong proficiency in Python, Linux and shell scripting.

● Experience with multi-node GPU/TPU clusters, CUDA, PyTorch Distributed, NCCL,

MPI/OpenMPI and distributed communication.

● Strong understanding of GPU/TPU architecture, distributed computing, profiling and

performance optimization.

● Experience with Docker, Kubernetes, Git and containerized development.

● Strong debugging, profiling and performance analysis skills.

Good to Have / Bonus Points

● JAX, XLA, MaxText, XPK, PJRT and TPU training infrastructure.

● Triton, Pallas or custom kernel optimization.

● Slurm, Ray, InfiniBand/RDMA and distributed storage systems.

● Experience with TensorBoard Profiler, Nsight Systems/Compute or XProf.

● Experience supporting large-scale LLM pretraining and distributed training at scale.

Why ZenteiQ

● Build real physics-native foundation models from scratch, not another LLM wrapper —

deep, defensible technical work.

● Be part of a nationally recognised mission: one of 8 startups selected under the

government's IndiaAI Mission to build a sovereign foundation model.

● Work alongside IISc-trained scientists and researchers, in a company founded by an IISc

professor.

● See your work land in real industry pilots across automotive, mobility, defence and

industrial R&D, with measurable impact.

● Join a lean, high-caliber team at an early, high-ownership stage.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.