Milind M.

Most AI projects don't fail because of bad models. They fail because the infrastructure around the model wasn't built for production. Fragile prototypes, unpredictable latency, cloud bills that explode at scale, no fallback when things break. That's the gap I close. I'm an AI systems engineer with 25 years of building things that have to work — embedded firmware, distributed systems, and now production AI infrastructure on AWS. I recently architected a production AI receptionist for clinics — handling appointment booking in Hindi and English over a messaging channel API, processing voice notes through a custom STT → intent classification → calendar action pipeline on AWS Lambda and DynamoDB, with sub-2s response times and no persistent server. That kind of end-to-end production constraint is what I work in. What I build: - LLM integration for environments with compliance, multilingual, or integration constraints - AWS serverless architecture for AI workloads — Lambda, DynamoDB, API Gateway, SQS - Messaging channel API pipelines for conversational AI on mobile platforms - Voice AI pipelines — custom STT/TTS architecture and platform integrations - IoT + AI systems for edge intelligence and on-device inference Where I add the most value: You have a working prototype and you're not sure it'll survive real traffic, real users, or a compliance review. Or you're hitting a wall with latency issues, language gaps, telephony quirks — and need someone who has been inside those problems. I come in, map what's actually broken, and fix it or rebuild it properly. How to start: Before committing to a full build, most engagements begin with an Architecture Audit. I review your existing system or concept, identify the failure points, and produce a concrete implementation roadmap. It's a low-risk way to find out exactly what you're dealing with before spending on a full build. If your AI system needs to work — not just demo — let's talk.