Technical Stack & Skillsets

A structured breakdown of the frameworks, architectures, and tools I use to design and deploy AI-driven software.

AI & LLM Systems

  • • Advanced Prompt Engineering
  • • RAG Pipeline Orchestration
  • • Context Window Tuning
  • • LLM Evaluation Frameworks

Agentic AI

  • • Multi-Agent Networks (CrewAI)
  • • Dynamic Tool-Calling Logic
  • • Persistent Memory Architecture
  • • Event-Driven LLM Control Loops

Vector & Databases

  • • pgvector Similarity Tuning
  • • Qdrant, Pinecone & FAISS
  • • PostgreSQL Schema Architecture
  • • Firebase, MongoDB & MySQL

Backend & APIs

  • • FastAPI & Node.js Rest Services
  • • Webhook Signatures & Security
  • • Rate-Limiting & Failover Routes
  • • Low-Latency Async Ingestion

ML Libraries

  • • NumPy, Pandas & Scikit-learn
  • • PyTorch & TensorFlow Models
  • • Cheminformatics & RDKit
  • • Structural Property Predictors

Workflow Automation

  • • n8n Custom Node Integrations
  • • Google Sheets & Docs API
  • • Telegram Webhook Bot States
  • • Gmail OAuth Sync Threads

Core Methodology

My research and engineering process heavily leverages advanced AI agents and code synthesis models. I treat AI tools as co-developers to rapidly explore design spaces, auto-generate unit testing strategies, and review system interfaces, accelerating development timelines from days to hours.

By combining AI-driven code generation with strict validation schemas (Pydantic, JSON Schema) and deterministic control logic, I ensure that the final product remains fully stable, clean, and production-ready.