LLM & Agentic AI Performance Engineer

About the role

We bridge the gap between teams that deeply understand silicon and benchmarking and teams building real-world AI solutions, turning performance insights into software optimizations that developers and customers can use. In this role, you will own end-to-end performance characterization and optimization of LLM and Agentic AI workloads, from Python-level frameworks and inference runtimes down to Linux, system architecture, and hardware performance counters.

What you'll do

• Characterize and optimize LLM and Agentic AI workloads across the complete software and hardware stack
• Identify performance bottlenecks across AI frameworks, inference runtimes, operating systems, memory, compute, and platform architecture
• Design and develop hardware-aware and hardware-agnostic optimizations for AI frameworks and runtimes
• Optimize LLM inference techniques including KV-cache management, attention, continuous batching, and quantization
• Work hands-on with serving stacks such as vLLM, SGLang, TGI, or equivalent technologies

What you need

• 1–3 years in data science & ai
• LLMs
• Python
• Linux
• C++
• C
• Rust
• M.Tech