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thinkingdev923/README.md

Thinking Developer

AI Engineer · Production ML & Generative AI Systems

AI engineering focus areas

I build, evaluate, and operate production ML and generative AI systems across model serving, RAG, AI agents, evaluation, and LLMOps.

GitHub Profile


Featured Work

Project Engineering Focus
TEN VoiceAgent Real-time multimodal conversational AI framework with voice assistants, WebSockets, RTC, speech-to-text, LLMs, text-to-speech, memory, turn detection, and AI agent extensions
EstateWise-ChatBot AI-powered real estate platform using Agentic AI, RAG, Pinecone, GraphRAG, Neo4j, MCP, LangGraph, property recommendations, and intelligent conversational workflows
Care Healthcare platform supporting patient management, hospital resources, inventory, telemedicine, triage, consultation history, real-time monitoring, and clinical data visualization
Veniqa Full-stack MEVN e-commerce platform with Node.js, Express.js, Vue.js, MongoDB, Redis, Stripe, authentication, inventory, order management, cloud storage, and Docker-based deployment
CapRover Application deployment and PaaS infrastructure built around Docker, Nginx, container orchestration, automated deployments, SSL, CLI tooling, and cloud-native workflows
Bubbles Real-time multi-agent web application with React, TypeScript, FastAPI, WebSockets, Redis Pub/Sub, asynchronous workers, state synchronization, and containerized microservices

What I Build

Area Engineering scope
Production ML & Generative AI Systems Model serving, inference workflows, APIs, background processing, containerized delivery, testing, deployment, monitoring, and operational integration
RAG & Retrieval Systems Ingestion, embeddings, vector and hybrid retrieval, reranking, retrieval evaluation, source attribution, grounded outputs, structured responses, and quality controls
AI Agents & Agentic Workflows Tool use, code execution, stateful workflows, validation, controlled execution, approvals, retries, failure recovery, executable checks, and behavior analysis
Evaluation & Benchmarking Multilingual benchmark design, model/LLM/agent/retrieval evaluation, executable checks, regression testing, trace analysis, model comparison, and failure diagnosis
Observability, Reliability & LLMOps Telemetry, tracing, monitoring, latency/cost analysis, routing behavior, drift signals, quality monitoring, failure visibility, and operational triage
Applied ML & Decision Systems Tabular ML, NLP, computer vision, calibration, threshold optimization, error analysis, explainability, risk scoring, and operator-facing decision support

🛠️ Core Stack

Area Technologies
Programming & Backend Python · SQL · FastAPI · Pydantic v2 · SQLAlchemy · Alembic · PostgreSQL · Redis · REST APIs · Linux · Bash · Git
Machine Learning & Data PyTorch · TensorFlow/Keras · scikit-learn · XGBoost · LightGBM · Pandas · NumPy · Polars · DuckDB
Generative AI, RAG & Retrieval Hugging Face Transformers · Sentence Transformers · Embeddings · Vector Search · Hybrid Retrieval · Reranking · pgvector · FAISS · Context Engineering · LangChain · OpenAI API · Ollama · vLLM
Agents, Evaluation & Benchmarking LangGraph · MCP · Tool Calling · Code Execution · LLM Evaluation · Agent Evaluation · Retrieval Evaluation · RAGAS · DeepEval · Benchmarking · Failure Analysis
MLOps, Testing & Infrastructure Docker · Docker Compose · RQ · MLflow · GitHub Actions · CI/CD · pytest · Ruff · mypy · Schema Validation · Model Serving & Inference · Caddy/Nginx
Observability & Reliability OpenTelemetry · Prometheus · Grafana · Structured Logging · Tracing · Model Monitoring · Latency/Cost Monitoring
Frontend & AI Interfaces TypeScript · JavaScript · React · Tailwind CSS · Streamlit · Gradio

Browse all public repositories →


🤝 Work & Collaboration

Open to AI engineering roles, remote contracts, and selected technical collaborations across:

  • Production ML & generative AI systems — model serving, APIs, deployment, evaluation, observability, reliability, and system integration
  • RAG, agents & AI evaluation — retrieval quality, grounded outputs, agentic workflows, tool execution, benchmark design, trace analysis, and failure diagnosis
  • Applied ML & decision systems — model evaluation, calibration, threshold policies, monitoring, analytics, and operator-facing workflows

Pinned Loading

  1. Bubbles Bubbles Public

    A satisfying interactive browser game built with vanilla JavaScript, HTML, and CSS, featuring smooth animations, dynamic DOM manipulation, responsive UI, event-driven gameplay, performance-focused …

    JavaScript 6

  2. Caprover Caprover Public

    Production-oriented PaaS for automated application deployment and infrastructure management. Built around Docker, Docker Swarm, Nginx, and Let's Encrypt, with CLI automation, web-based management, …

    TypeScript 6

  3. Care Care Public

    Healthcare management platform combining patient management, hospital resource analytics, inventory monitoring, telemedicine, triage, real-time patient monitoring, and clinical data visualization, …

    Python 6

  4. EstateWise-ChatBot EstateWise-ChatBot Public

    🏠 An AI real estate app featuring secure auth, conversation management, & personalized property recommendations. Powered by Agentic AI, RAG (w/ Pinecone), GraphRAG (w/ Neo4j), MCP, kNN, K‑Means Clu…

    TypeScript 6

  5. Ten-VoiceAgent Ten-VoiceAgent Public

    Real-time multimodal conversational AI framework for building low-latency voice and video agents. Integrates LLMs, STT, TTS, WebSockets, RTC, VAD, turn detection, memory, speaker diarization, SIP, …

    Python 6

  6. Veniqa Veniqa Public

    MEVN Full Stack E-Commerce Solution. Built using MEVN Stack (Node.js, Express.js, Vue.js, MongoDB) with Developer Friendliness and Cloud Integrations in mind. Previously Powered the Veniqa New York…

    JavaScript 6