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I build production AI systems that reason, plan, and execute autonomously — from multi-agent orchestration to enterprise RAG pipelines, LoRA fine-tuning at scale, and multi-adapter inference serving. 🔭 Currently Building:
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Production multi-agent orchestration with Google ADK — coordinator, planner, coder & reviewer agents
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Knowledge Graph + RAG with Neo4j for intelligent document QA
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Hybrid search RAG with guardrails, reranking & Langfuse observability
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Natural language → SQL with self-correction & multi-dialect support
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Build language models from zero — tokenizer, transformer, training, alignment
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Production LoRA/QLoRA fine-tuning — YAML recipes, MLflow, vLLM serving
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One base model, many adapters per request — OpenAI-compatible inference gateway
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Adapter lifecycle — train, evaluate, merge (TIES/DARE), version & publish
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Specialize LLMs for medical, legal, finance & code — domain benchmarks, curriculum training, safety guardrails
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ML-powered insurance fraud detection — 10 expert rules, PyCaret AutoML, explainable decisions
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📋 Full Tech Breakdown
LLM Providers OpenAI • Anthropic • Google Gemini • Llama • Mistral
Agent Frameworks Google ADK • A2A Protocol • MCP Tools • LangGraph • CrewAI
RAG Stack LlamaIndex • LangChain • Neo4j • OpenSearch • Pinecone • Weaviate
Observability Langfuse • MLflow • Weights & Biases • OpenTelemetry
Inference vLLM • Multi-LoRA Serving • TensorRT-LLM • ONNX Runtime
Fine-tuning LoRA • QLoRA • DoRA • Unsloth • Axolotl • DeepSpeed • RLHF/DPO
Model Building PyTorch Transformers • BPE Tokenizers • GGUF/ONNX Export
Frontend React • Vite • Next.js • TypeScript • TailwindCSS
Backend FastAPI • Python • Node.js • GraphQL
Cloud AWS (Bedrock, SageMaker) • GCP (Vertex AI) • Azure
Infrastructure Docker • Kubernetes • Terraform • GitHub Actions
