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Achievement: Pair Extraordinairex2Achievement: QuickdrawAchievement: Pull Shark

Achievements

Achievement: Pair Extraordinairex2Achievement: QuickdrawAchievement: Pull Shark

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GopalGB/README.md

Gopal Bagaswar

Applied AI Engineer | RAG | Agent Systems | Production LLM Applications

Portfolio LinkedIn X Email

I am moving from senior data analytics into applied AI engineering. I build testable systems around retrieval, agent workflows, analytics, and developer automation, with an emphasis on evidence, evaluation, and guardrails.

Selected Projects

Project Engineering proof Stack
citipoints Retail loyalty analytics with ML models and an auditable natural-language-to-SQL layer over synthetic data TypeScript, Next.js, FastAPI, XGBoost
rag-analytics-assistant Guardrailed analytics assistant with hybrid retrieval and tool-calling over user-controlled data Python, FastAPI, DuckDB, RAG
codex-upgrade Portable Codex CLI skills, prompts, and operating guardrails Python, Codex CLI
claude-code-toolkit Hooks and reusable controls for session management and credential protection JavaScript, Claude Code
officehub Self-hosted project workspace with timelines and management rollups TypeScript, Next.js, Postgres, Docker
ai-engineering-receipts Public interview playbook and evidence index for applied-AI work Documentation, evidence maps

Verified Open-Source Contributions

Six merged code pull requests, verified against the upstream repositories:

Repository Contribution
gradio-app/gradio#13396 Convert non-WAV audio output to int16 to prevent playback corruption
gradio-app/gradio#13344 Fix a KeyError involving nested gr.Examples inside gr.render
livekit/agents#5288 Make function-call history preservation configurable in AgentTask
mem0ai/mem0#4461 Add reasoning_effort support for reasoning models
optuna/optuna-integration#278 Fix late binding in BoTorch constraint closures
Mintplex-Labs/anything-llm#5560 Strip Markdown before text-to-speech processing

Engineering Focus

Applied AI : RAG, agent workflows, tool calling, evaluation, guardrails
Backend    : Python, FastAPI, Node.js, PostgreSQL
Frontend   : TypeScript, React, Next.js
Data       : Pandas, SQL, forecasting, segmentation, experimentation
Delivery   : Docker, CI, reproducible validation, evidence-first documentation

Current Direction

  • Building public, reviewable AI-engineering proof rather than tutorial clones.
  • Contributing tested fixes to the open-source tools used in real projects.
  • Looking for applied AI, AI engineering, and AI analytics roles open to remote work from India.

Pinned Loading

  1. codex-upgrade codex-upgrade Public

    Portable zero-MCP power kit for OpenAI Codex CLI — expert skills (big-Excel/PPTX/PDF/research/patents/ML/Python), /prompts:* slash commands, and an operating-law AGENTS.md. One install wires it int…

    Python

  2. rag-analytics-assistant rag-analytics-assistant Public

    LLM-first, guardrailed RAG analytics assistant over your own CSV + documents (FastAPI · DuckDB · tool-calling agent · hybrid retrieval)

    Python

  3. ai-engineering-receipts ai-engineering-receipts Public

    Public FAANG AI Engineer interview playbook + Q-bank + handoff JSON. Proof, not claims. Receipts from MAX HQ, GetCallBounce, CitiPoints, and the 32-day hardening sprint.

  4. citipoints citipoints Public

    ML-augmented retail loyalty analytics — Next.js 15 + FastAPI + XGBoost/BG-NBD/FP-Growth, with an auditable natural-language (NL→SQL) AI chat layer. Synthetic data.

    TypeScript

  5. claude-code-toolkit claude-code-toolkit Public

    Production-ready hooks, skills, and configurations for Claude Code — session management, auto-briefings, credential protection, and more

    JavaScript 1

  6. officehub officehub Public

    Self-hosted Notion-like office workspace — projects, timelines, enhancements, manager rollup. Next.js 15 + Postgres + Docker.

    TypeScript