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Building and shipping AI systems with teams.
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Building and shipping AI systems with teams.

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fahmidme/README.md

Fahmid Uddin

Forward-Deployed Product Engineer — Building and Shipping AI Systems with Teams.

Toronto-based founder-operator and hands-on engineer with 15 years of experience across frontend, backend, cloud systems, UX, infrastructure, automation, and creative technology.

I work from ambiguous operating problems through discovery, architecture, implementation, deployment, observability, and iteration.

fahmid.com · LinkedIn · X · Email

Current work

  • AI Account Executive — 650+ tracked hours delivering a client AI sales platform through workflow discovery, implementation, production releases, integrations, billing, identity, administration, and observability.
  • Optifly — AI-native travel planning with shareable itineraries, live inventory, and partner routing. Recent verified signals: 11,270 share-landing views, 13,362 itinerary loads, 201 booking clicks, and 90% plan progression.
  • Nexore / xOS — an independent AI systems studio and a self-hosted control plane used in my own product and client work for agent workflows, semantic memory, MCP tooling, isolated sessions, and permission-aware operations.

Selected engineering work

  • AgentCore AG-UI Starter — streams a Strands agent from Amazon Bedrock AgentCore into a Next.js and CopilotKit interface over AG-UI, with architecture documentation, security boundaries, CI, and a production roadmap.
  • Incident Triage API — FastAPI incident-ticket search and triage with SQLite FTS5 retrieval, grounded responses, citation validation, traces, tests, and synthetic incident data.
  • Nano Banana MCP — TypeScript MCP server for Gemini image generation on Vertex AI with GCS references, progress notifications, and long-running task support.
  • Worktree Parallel — a Codex skill for running isolated development work across parallel Git worktrees.

Earlier work

  • Gitfolio — built and sold an AI résumé-builder SaaS.
  • Metagood — first engineering hire; helped launch OnChainMonkey.
  • Decise.io — delivered ordering experiences for City BBQ, Pieology, and Chipotle.
  • LegixAI — introduced a RAG architecture that reduced LLM pipeline cost by approximately 15× and increased processing speed by approximately 20×.

Working with

TypeScript · React · Next.js · Node.js · Python · AWS · GCP · RAG · MCP · agent workflows · evals · traces

Pinned Loading

  1. agentcore-agui-starter agentcore-agui-starter Public

    Streaming Strands agents from Amazon Bedrock AgentCore to a Next.js CopilotKit UI over AG-UI.

    TypeScript 13

  2. incident-triage-api incident-triage-api Public

    FastAPI incident-ticket search and triage API with SQLite FTS5, grounded OpenAI assist responses, traces, tests, and synthetic data.

    Python 1

  3. worktree-parallel worktree-parallel Public

    Create and manage a git worktree for parallel feature development, then open the new worktree in the editor (code/Cursor) for a second Codex session.

    34 4

  4. nano-banana-mcp nano-banana-mcp Public

    TypeScript 9 5

  5. ai-screenshot-renamer ai-screenshot-renamer Public

    This project automatically renames screenshots using AI-generated descriptions. It's designed to work with macOS and uses OpenAI's GPT-4 Vision model to analyze and describe screenshots.

    Python 3