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.