AI Engineer · LLM Agents · Voice AI · Testing & Evaluation

I build LLM agents and voice AI products, and I measure them before I trust them. Right now I'm building Katha, an iOS reader that gives every character in a book its own AI voice. Before that I launched indes.ai, a multi-LLM platform with agentic routing.

Katha: Multi-Voice AI Reader for iOS · in development

• Agentic voice casting: Claude profiles each character from the book and writes a voice prompt; Qwen3-TTS renders candidates until one passes a pitch check, then it becomes that character's voice
• Speaker attribution (NER, alias resolution, Claude as LLM fallback), measured on a 380-quote annotated benchmark: explicit-quote accuracy 0.68 → 0.82 at 95% precision
• 4 TTS engines routed by language: Kokoro on-device (MLX), self-hosted Qwen3-TTS and Indic Parler-TTS, Apple as fallback, with consent-gated voice cloning
• 1,500+ Swift tests and 150+ Pytest tests in CI · Swift, Python, Claude API, MLX

indes.ai: Multi-LLM Platform with Agentic Routing

• Streams GPT, Claude, Gemini and DeepSeek side by side and merges them into one answer (web, iOS/Android)
• Agentic routing: 11-type query classifier with LLM fallback, plus a 7-tool orchestrator that answers simple queries without an LLM call
• Fine-tuned a T5 synthesis model on Claude outputs, evaluated with BLEU/ROUGE
• 650+ Jest/Supertest tests gating every PR, Playwright E2E across Chromium, Firefox and WebKit · Node.js, React Native, Python

aria: LLM red-teaming agent

• 10 attack strategies, 77 variants
• Reflexion-based learning from failed attempts

reflexive-llm-agent: self-reflective tool-using agent

• ReAct + Reflexion loop with long-term ChromaDB memory
• FastAPI backend and Streamlit UI

• vllm-project/llm-compressor#2330: GSM8K evaluation script and AWQ + FP8 results (merged)
• instructlab/training#682: fix HYBRID_SHARD failure when world_size < available GPUs (merged)

• triton-kernels: FlashAttention, MatMul, Softmax and LayerNorm in Triton, autotuned for T4/V100/A100/H100
• ddp-lora-trainer: multi-GPU LoRA fine-tuning with PyTorch DDP and mixed precision
• llm-quantization · llm-sparsity: GPTQ, AWQ, SmoothQuant; magnitude, 2:4 and WANDA pruning
• vibeclean-ai: AST-based security analysis for AI-generated code
• cuda-mempool · lockfree-queue · arena-alloc: CUDA, Rust and C++ systems work

• Agents & LLMs: Claude, GPT, Gemini APIs · tool calling · agentic routing · RAG · ReAct · LangChain · MCP
• Voice AI: Qwen3-TTS · Indic Parler-TTS · Kokoro · Chatterbox · MLX
• Testing & Evaluation: Playwright · Jest · Pytest · Swift Testing · load testing · eval sets · BLEU/ROUGE
• Languages: Python · Swift · JavaScript/Node.js · SQL · C++ · Rust
• Infrastructure: Google Cloud · AWS · Docker · GitHub Actions · Redis · PostgreSQL · Kafka