Building Production-Ready AI Systems
I mainly work with AI agents, LangChain, RAG, and Python backends. I don't just connect an AI model and call it done. I build the rest of the system around it too, including the retrieval, API, and database, so everything works together properly. My focus is on building AI systems that are actually reliable and usable in real projects.
An enterprise PropTech pipeline combined with an autonomous, tool-using AI Analyst. It orchestrates the harvesting of unstructured global property listings using headless Playwright automation, normalizes them via a Pandas data warehouse, and stores them via SQLAlchemy. Features a Zero-Framework ReAct loop binding to Groq's Llama 3.3 70B, allowing the agent to route execution between SQL toolkits, live web gateways, and deterministic financial calculators.
An autonomous AI analyst for e-commerce. Aisle ingests store data into a consolidated warehouse and places a multi-tool AI agent on top. Uses a native tool-calling loop (Groq/Llama 3) with 12 tools to forecast demand, recommend pricing, and segment customers (RFM analysis). Responses stream token-by-token to a premium Next.js dashboard.
An end-to-end Data Engineering pipeline that automatically extracts e-commerce pricing data via Python Requests, normalizes and enforces strict typing using Pandas, and loads it into a relational SQLite database via SQLAlchemy. Exposes secure REST API endpoints via FastAPI to serve real-time analytics.
An autonomous agent taking plain-English instructions to extract validated JSON from any website. Uses a two-stage fetch architecture to save costs. It dynamically generates extraction schemas at runtime and is fully SSRF-hardened by validating IPs against loopback and cloud-metadata ranges.
Extracts live job market data from isolated sources (REST APIs, Web Scrapers via BeautifulSoup, static CSVs), cleans it with strict Pandas logic, and unifies it into a single PostgreSQL Data Warehouse. Served via FastAPI to a responsive Tailwind dashboard.
An enterprise-grade, offline Client-Server architecture for high-security aerospace facilities. Bypasses the cloud by establishing an asynchronous HTTP polling network over a local subnet. Features an automated Pandas ETL engine to ingest messy CSV question banks.
Enterprise FastAPI backend for secure, high-concurrency asynchronous data processing. Implements strict OAuth2/JWT authentication, Alembic migrations, and Domain-Driven Design (DDD). Uses FastAPI BackgroundTasks and Pandas to process massive CSV workloads instantly.
Maintaining a 100% Job Success Score. Designed and delivered LLM agent orchestration systems, automated extraction pipelines, and robust APIs. Replaced manual reporting workflows with deterministic Python logic for global clients.
Collaborated with the engineering team to optimize backend infrastructure using advanced Python and FastAPI. Cleaned datasets, wrote SQL queries, and gained hands-on commercial experience deploying scalable applications.
I'm currently available for freelance projects and full-time opportunities. Whether you need a production-ready RAG system, an autonomous AI agent, or a complex ETL pipeline, let's talk about how we can build it.