ML Infrastructure & Acceleration Engineer - LLMs

Who We Are You've ordered dinner through Just Eat Takeaway. You've bought and sold on OLX. You've paid with PayU. Chances are, you've already used a Prosus company today. We are Prosus, a global technology company powering some of the world's leading lifestyle ecommerce businesses. Every day, billions of people across India, Latin America, Europe and beyond use our products and services to buy, sell, pay, connect and discover. Our AI-driven ecosystem spans 100+ companies across ecommerce, food delivery, fintech and experiences, serving billions of consumers worldwide. AI powers how we operate and innovate, with more than 60,000 active AI agents helping our teams build, scale and move faster. Through our investment team, we also back the founders and technologies shaping the future: From robotics and autonomous systems to synthetic biology and frontier AI. Guided by our five values Innovation, Results, People, Impact, and Entrepreneurship, culture is our competitive advantage. We thrive with experimentation; we build, we test, we learn, we iterate. Join us in shaping the future of technology at global scale. Who we're looking for We're seeking an experienced ML Infrastructure & Acceleration Engineer to own the GPU infrastructure that powers our LLM training and serving, and to make every workload on it faster and cheaper. You'll run our H200 cluster end to end, deploy models to production, and profile and optimize our training and inference pipelines down to the kernel level when it matters. You understand GPUs from the cluster scheduler to the memory hierarchy, you measure before you optimize, and you're motivated by turning raw compute into faster iteration for our ML team. You thrive in environments where infrastructure reliability and efficiency directly shape how quickly research reaches millions of users. What you'll do GPU Cluster & Infrastructure: Own our H200 GPU cluster end to end: Kubernetes and Slurm for training and inference workloads, with multi-tenant scheduling, quotas, and resource isolation Keep large distributed training runs healthy: node health checks, NCCL and interconnect debugging, automated job recovery, and checkpoint management Track and optimize GPU utilization and cost across training and inference, with dashboards and budget alerting Manage infrastructure-as-code and provisioning for GPU environments Define and monitor SLIs/SLOs for cluster availability and production model serving Deployment & Serving: Deploy LLMs to production using vLLM or SGLang, with CI/CD, model versioning, and safe rollouts and rollbacks Monitor serving latency, throughput, errors, and cost, and act on regressions ML Acceleration: Profile training and inference end to end (PyTorch Profiler, Nsight Systems, Nsight Compute) to find and remove bottlenecks Improve training throughput and MFU: parallelism strategies (FSDP, tensor and pipeline parallelism), communication/compute overlap, mixed precision and FP8, activation checkpointing, torch.compile Optimize inference: quantization (FP8, INT8, INT4), speculative decoding, prefix caching, continuous batching, KV cache tuning, and parallelism layouts Write or integrate fused and custom kernels (Triton, CUDA) where off-the-shelf implementations fall short Build benchmarking tooling so every optimization ships with measured gains Enablement & Best Practices: Partner with ML engineers and researchers to make training and serving efficient by default Establish best practices for using the cluster and guide teams in their adoption Minimum qualifications 5+ years in ML infrastructure, MLOps, HPC, or platform engineering with focus on GPU workloads Hands-on experience operating multi-node GPU clusters with Kubernetes and/or Slurm, including debugging hardware, networking (NCCL, InfiniBand/RoCE), and job failures Production experience deploying and optimizing LLM inference with vLLM, SGLang, or TensorRT-LLM Proven track record of p