Shobhit S.

AI App Developer | Flutter AI | On-Device AI | Streaming & Real-Time | SaaS Architect I'm Shobhit Sharma, a Principal Full-Stack AI Product Engineer with 10+ years of experience building AI-powered applications, SaaS platforms, mobile products, and real-time systems. I help founders turn complex requirements into production-ready AI apps, SaaS, mobile products, fantasy sports, and streaming platforms—working across architecture, APIs, integrations, and deployment. AI APPLICATIONS, LLMs & AI SAAS I build AI-powered products using OpenAI, LLMs, Retrieval-Augmented Generation (RAG), AI agents, embeddings, vector search, OCR, document intelligence, and computer vision. My focus includes Production LLM Engineering, End-to-End AI SaaS Engineering, AI Product Architecture, and Production AI System Architecture. I work on AI Observability with Langfuse, LLM Inference Cost Optimization, LLM Evaluation & Reliability Engineering, retrieval quality, workflow tracing, and failure analysis. ON-DEVICE AI & COMPUTER VISION I work with on-device AI, edge inference, TensorFlow Lite, YOLO, camera-based AI, image recognition, and model inference for mobile experiences. Relevant challenges include mobile model deployment, latency-aware inference, and integrating computer vision into practical user workflows. FLUTTER & FULL-STACK PRODUCT ENGINEERING I build cross-platform iOS and Android apps with Flutter and Dart, with React Native also in my stack. Backend and web technologies include Node.js, NestJS, Express, React, Next.js, TypeScript, REST APIs, GraphQL, Firebase, PostgreSQL, MongoDB, Redis, authentication, payments, push notifications, and WebSockets. I connect mobile UX, API orchestration, backend logic, databases, and third-party services into complete products. VIDEO STREAMING, OTT & REAL-TIME SYSTEMS I work on live streaming, video-on-demand (VOD), OTT applications, media processing, and real-time communication. Technologies include FFmpeg, HLS, RTMP, WebRTC, transcoding, adaptive bitrate streaming, media pipelines, WebSockets, Socket .IO, and Redis. I focus on dependable stream delivery, media processing, low-latency communication, and real-time interactions. SAAS, FINTECH & SYSTEM ARCHITECTURE I architect SaaS platforms, multi-tenant applications, marketplaces, subscription products, and business software. Areas include tenant isolation, role-based access control (RBAC), database architecture, billing, Stripe and Stripe Connect, payment APIs, webhooks, and transaction-safe workflows. I focus on tenant boundaries, webhook idempotency, concurrent updates, access control, and transaction integrity. INDUSTRY & PROJECT EXPERIENCE SportsTech & large scale fantasy sports: Sports APIs, live scores, player data, competitions, leaderboards, and real-time game logic. Related projects include SimPlay, Knockout11, and an NFL fantasy sports platform. Healthcare & telehealth: Patient and provider applications, appointment workflows, video consultations, healthcare integrations, and real-time communication. Project experience includes Doctor Anywhere. Entertainment & OTT: Streaming platforms, subscriptions, VOD, live media, and video processing, including Melanflix. AI SaaS & marketplaces: Intelligent workflows, subscription platforms, mobile products, and complex business applications. HOW I APPROACH ENGINEERING I design for real-world failure cases—not just the happy path. That means considering duplicate payment events, concurrent updates, external API outages, inconsistent AI outputs, and costly inference before they become production issues. I combine development with architecture, testing, deployment, and observability. AI coding tools can speed up work, but code still needs engineering judgment, review, and testing. Core stack: OpenAI, LLMs, RAG, AI agents, Langfuse, TensorFlow Lite, YOLO, Flutter, Dart, React Native, React, Next.js, TypeScript, Node.js, NestJS, PostgreSQL, MongoDB, Redis, FFmpeg, HLS, RTMP, WebRTC, AWS, Google Cloud, Docker, Stripe, and Stripe Connect. Additional expertise includes prompt engineering, structured outputs, tool calling, AI agent workflows, retrieval pipelines, embeddings, semantic search, LangChain, LlamaIndex, pgvector, and Pinecone. For on-device AI, I consider model size, inference latency, memory, offline capability, and camera-to-model pipelines. Full-stack engineering includes state management, SDK integration, secure authentication, API lifecycle design, background processing, caching, and database optimization. Streaming expertise covers encoding, transcoding, HLS, RTMP, WebRTC, adaptive bitrate playback, media storage, and end-to-end media pipelines. SaaS and fintech architecture includes subscription lifecycle management, payment reconciliation, auditability, background jobs. LET'S TALK ABOUT YOUR PRODUCT Building an AI app, Flutter product, fantasy sports platform, OTT service, or SaaS? Tell me what you're building and what needs solving. Shobhit Sharma