Umer S.

If your AI works in a demo but falls apart on real data, that gap is exactly what I fix. With 8+ years across AI platforms, SaaS products, and workflow automation, I build AI-powered applications that handle real users and real workflows: RAG chatbots grounded in your data, autonomous AI agents, AI voice agents for outbound and inbound calls, and automation that replaces manual operations. I work primarily in Python and Django, with FastAPI, Node.js, and Next.js across the stack, and my focus is reliable, production-safe AI with structured, validated outputs, not prompt experiments that break under real usage. Most AI projects fail in the same place: the model works in a notebook, then breaks on messy real-world data and real edge cases. My job is the engineering around the model, retrieval, grounding, validation, and clean architecture, that turns a prototype into a system a business can run on. Recent builds: → An AI chatbot and companion platform where I built the RAG pipeline and grounded, context-aware conversations over a curated knowledge base. → A live AI voice and chat CRM where I built the agent layer with tool calling and human-in-the-loop review. → An autonomous AI voice agent for outbound calling, running in production. → A multi-tenant SaaS backend with RBAC, billing, and APIs built to scale. → An AI document-processing pipeline that turns uploads into structured, downloadable reports. (Live portfolio links shared on request.) Core stack: Python, Django, FastAPI, Node.js, Nest.js, TypeScript, Next.js, React, PostgreSQL, AWS, Docker. RAG and LLM work on OpenAI, Claude, and Bedrock. * AI systems and LLM integrations: RAG pipelines with vector databases, embedding, chunking, and relevance tuning AI chatbots and copilots grounded in internal data and knowledge bases AI agents and multi-agent workflows with tool calling, function calling, and multi-step reasoning OpenAI (GPT-4), Anthropic Claude, and AWS Bedrock integrations LangChain and LangGraph orchestration AI voice agents (inbound and outbound calling, real-time conversation) Structured AI outputs with validation layers and hallucination reduction Streaming AI systems (SSE, WebSockets) * AI automation and integrations: Workflow automation with n8n, Make, and Zapier API integrations, webhooks, and event-driven pipelines AI-assisted dashboards and internal tools * Full-stack SaaS development: Python and Django REST backends, async processing, and API design Next.js, React, TypeScript (SSR, App Router, performance) Node.js, Nest.js, FastAPI backends REST APIs, microservices, async processing Multi-tenant SaaS with RBAC, billing, and Stripe * Cloud and infrastructure: AWS (Lambda, ECS / Fargate, RDS, S3, Bedrock) Docker, CI/CD, Vercel Logging, monitoring, and production optimization How I work: I treat projects as systems, not isolated features: clean architecture from day one, reliable backend logic, controlled and validated AI outputs, responsive frontends, and clear communication. I own delivery end to end, from architecture and APIs through deployment and production readiness. If you are building an AI chatbot, RAG system, AI voice agent, AI automation, a SaaS platform, or a scalable full-stack app, I can help you build it properly and ship with confidence. Click "Invite to Job" and let's discuss your project.