Aru B.

I design and deploy production-grade AI systems: from data pipelines to scalable LLM applications. With 7+ years in ML/AI, I specialize in LLM systems, RAG architectures, and MLOps, helping teams move from prototype to reliable, scalable AI in production. My focus is not just building models but ensuring they perform under real-world constraints: latency, cost, hallucination control, and continuous improvement. What I build -End-to-end ML pipelines: ingestion, training, deployment, monitoring -RAG systems with proper evaluation harnesses (retrieval quality, regression catching) -LLM observability: prompt versioning, tracing, feedback loops -Scalable inference (real-time and batch) with GPU optimization -CI/CD for ML with MLflow, Airflow, automated retraining -AI agents with guardrails and reliable tool use Selected results -Fraud detection pipeline (real-time inference) → ~$890K in losses prevented -Forecasting system using ensemble models → 31% accuracy lift over baseline -Multiple LLM products running in production under client SLAs -$3M+ in measurable business impact across deployments (happy to walk through specifics on a call) Stack -Languages/frameworks: Python, FastAPI -LLMs: OpenAI, Anthropic, HuggingFace, LangChain, LlamaIndex -Vector DBs: Pinecone, Weaviate, ChromaDB, FAISS -Infra: AWS, Docker, Supabase, MLflow, Airflow How I work -Replies in 0–4 hours on business days -Production-grade code with tests and docs, not throwaway notebooks -Clear scope, direct communication, focused on business outcomes -Revenue, automation, speed-to-market — that's the scoreboard Let's turn your AI idea into a working product. Send an invite with your project details and I'll get back to you with a clear plan.