Pradeep K

AI Engineer · Faster Inference · More Reliable Agents

I build efficient AI systems, from GPU kernels and LLM serving to grounded, observable agent products.

Bengaluru, India · Open to AI Engineer, AI Inference Engineer, LLM Engineer, and GenAI roles

I focus on the engineering details that make AI useful in the real world: latency, cost, evaluation, reliability, and deployment.

An AI assistant for AMD ROCm developers. It reads profiler output or training metrics, explains likely GPU bottlenecks, and suggests concrete optimizations.

Explore: Live demo · README · SFT training · Benchmarking

LoRA DPO MI300X ROCm vLLM Gradio

A low-level inference case study covering MXFP4 GEMM, MoE MXFP4, and Mixed MLA decode. It documents quantization-aware dispatch, runtime-path tuning, metadata reuse, and benchmark-driven iteration while preserving correctness.

Explore: README · MXFP4 GEMM · MoE MXFP4 · Mixed MLA · Benchmark summary

FP8 MXFP4 GEMM Kernel Optimization Latency Benchmarking

An OpenEnv benchmark for agents working with stale or conflicting knowledge. The agent detects hallucinations, identifies outdated sources, repairs the knowledge base, and verifies corrected answers.

Explore: Live demo · API docs · README · Inference · Task suite

RAG Evaluation OpenEnv Grounding AI Safety

An API-first assistant that turns a natural-language goal into a structured workflow. Agents retrieve context, plan work, create tasks and notes, schedule calendar events, and return clean results through FastAPI.

Explore: README · Agent workflow · MCP tools

FastAPI Gemini Google ADK MCP AlloyDB

• FlashAttention, KV-cache optimization, Triton, and vLLM internals
• Distributed inference, serving systems, and ML systems design
• Evaluation methods for grounded and reliable AI agents

Building efficient, scalable AI systems, from GPU kernels to intelligent agents.