Portfolio 

This is not just RAG. It is a controlled, production-shaped system for answering questions over real SEC 10-K / 10-Q filings with VERIFIABLE CITATIONS.

The workflow is intentionally ULTRA OPTIMIZED for latency + cost: a DETERMINISTIC ROUTER classifies intent (single-company, comparison, ingestion) with NO LLM CALL, then a FAST RAG AGENT compresses retrieval + analysis + reporting into ONE LLM call (about 3× fewer calls than traditional chains).

Retrieval runs on PINECONE (SERVERLESS) with sentence-transformer embeddings, and advanced options of HYBRID RETRIEVAL, SECTION BOOSTING, RERANKING + QUERY REWRITING. An MD5-KEYED SEMANTIC CACHE returns repeated queries in ~20ms.

Ships with a MULTI-PROVIDER LLM abstraction (OpenAI / Anthropic / Ollama) with pooled + cached instances, plus performance engineering via async FastAPI, request deduplication, and concurrent comparison execution.

A production-style policy compliance assistant that answers questions from internal policy documents with REAL CITATIONS, REAL-TIME STREAMING, and optional MULTIMODAL (text + images) support using CLIP library.

The RAG pipeline is orchestrated with LANGGRAPH + LANGCHAIN and follows a clear flow (query embedding → vector search → top-k retrieval → context assembly → LLM generation → citation extraction). Retrieval runs on PINECONE (SERVERLESS) with semantic search + metadata filtering, enhanced with QUERY EXPANSION, HYBRID SEARCH (semantic + keyword), and relevance boosting via RERANKING using a CROSS-ENCODER(plus optional AUTO REWRITE to refine user queries). A REDIS caching layer reduces repeated retrieval/LLM calls for faster, more consistent answers. Supports a unified MULTI-PROVIDER interface (Ollama, OpenAI, Anthropic) and streams token-by-token with SSE. Every answer includes citations + is recorded to POSTGRESQL AUDIT LOGS for traceability; shipped with a production-friendly DOCKER COMPOSE setup.

An agentic AI travel-planning platform that generates fully personalized itineraries using a MULTI-AGENT system orchestrated by LANGGRAPH. A SUPERVISOR agent analyzes user intent and delegates work across SPECIALIZED AGENTS (RESEARCH, LOGISTICS, COMPLIANCE, EXPERIENCE)to produce structured, consistent plans.

Plans are enriched using real travel data via the Amadeus API to ensure locations are accurate and realistic.

The orchestration runs in a separate FASTAPI service deployed on AWS LAMBDA for stateless, pay-per-use scaling, enriched with real travel data via AMADEUS API. Trips persist to POSTGRESQL (NEON) with PRISMA, while the Next.js UI supports planning, saving, and reviewing across devices. Reliability comes from retries + structured flows and LANGSMITH debugging.

Tech: Next.js • React 18 • FastAPI • AWS Lambda • LangGraph • LangChain • OpenAI • Postgre SQL (Neon) • Prisma • Tailwind CSS • Clerk • TanStack Query • Amadeus API

Enterprise Scale project intentionally built to showcase AGENTIC AI patterns inside a real e-commerce app. The React frontend talks to an AGENT GATEWAY through a single chat surface. The agent performs INTENT ROUTING and delegates to either an internal MCP TOOL SERVER (explicit tools for product search/lookup + order tracking) or a RAG SERVICE (local docs retrieval for policy/support Q&A).

This is a production-style full-stack e-commerce application built using React, FASTAPI, MONGODB, and DOCKER. It provides a complete end-to-end shopping experience—from user authentication and product browsing to secure checkout and order tracking—all backed by scalable async REST APIs and ROLE-BASED admin management.

The system includes a fully functional admin dashboard for managing users, products, orders, and inventory, along with secure JWT-based authentication, image upload support, and PAYPAL + STRIPE payment integration. 🛡️ The frontend communicates with a containerized backend API and MongoDB database, forming a deployable, cloud-ready full-stack solution.

The system understands intent and automatically chooses the correct backend action. 🤖 The key production-shaped boundary is that the UI never calls TOOLS or RETRIEVAL directly—everything stays behind the GATEWAY—making the tool-use layer AUDITABLE and TESTABLE, while enabling optional HYBRID SEARCH (BM25 + embeddings) via PINECONE/OPENAI.

A reproducible, end-to-end fine-tuning pipeline for building a domain-specialized POLICY COMPLIANCE model using QLORA (4-bit quantization), then deploying it for local inference with OLLAMA. Built for repeatable experiments and real deployment, from dataset → fine-tune → packaging.

Implements parameter-efficient fine-tuning of LLAMA 3.1 8B with 4-BIT NF4 quantization—enabling training on a SINGLE GPU. Policy answer accuracy improved by about POLICY ANSWER ACCURACY IMPROVED BY ABOUT ~70% versus the base model, with strong convergence (training loss reduced by ~79% over 3 EPOCHS). Includes an automated EVALUATION HARNESS, a clean END-TO-END PIPELINE (data generation → fine-tuning → adapter merge → evaluation → packaging), and config-driven experiments with clean artifact separation. Ships a merged model packaged for OLLAMA (adapter merge → GGUF) to support fast, OFFLINE INFERENCE.

Tech: Python • HuggingFace Transformers • PEFT (LoRA / QLoRA) • BitsAndBytes • Accelerate • Ollama • GGUF

A production-ready FINE-TUNING and evaluation pipeline for GPT-4O-MINI, designed to improve enterprise policy compliance accuracy while reducing TOKEN USAGE and LATENCY. The domain-specialized model trained on 164 curated JSONL examples boosted accuracy from 35% → 77.5% (~2.2× better) on compliance-focused evaluation sets. Includes an AUTOMATED EVALUATION SUITE with CATEGORY-LEVEL SCORING, REGRESSION CHECKS, and performance reports—token usage dropped by ~20%, and response latency improved from 1.61s → 1.06s (~44% faster). The pipeline follows an MLOPS-STYLE WORKFLOW with training data versioning, job automation, and model validation in OPENAI PLAYGROUND.

A fully SERVERLESS, EVENT-DRIVEN Order Management System on AWS, built for high scalability, operational visibility, and real-time analytics. Order workflows run through AWS LAMBDA behind API GATEWAY, with decoupled processing via SQS, SNS, and DYNAMODB.

Complex lifecycle flows are coordinated with STEP FUNCTIONS (retries, failure paths, DLQs) and optional EVENTBRIDGE pipelines. The whole environment is provisioned with AWS CDK (TypeScript) + CloudFormation, and shipped with a modern REACT + TYPESCRIPT dashboard featuring analytics charts (Chart.js) and CSV import/export workflows. Security and observability lean on least-privilege IAM + CloudWatch monitoring.

Tech: AWS Lambda • API Gateway • SQS • SNS • DynamoDB • S3 • Step Functions • EventBridge • AWS CDK • React 19 • TypeScript • Chart.js

Mixmaster is a full-stack cocktail discovery app built with React ⚛️, React Query ⚡, Node.js 🌐, and PostgreSQL 🗄️. It blends real-time data from TheCocktailDB 🍸 with custom user-created recipes stored locally with uploading your image to S3 bucket🪣, enhanced by intelligent caching for high-speed performance. A serverless AWS Lambda microservice ☁️ handles newsletter automation with Amazon SES ✉️, delivering a smooth, modern UI and fast, reliable CRUD operations.

A fully Dockerized MERN e-commerce platform built with React ⚛️ + Redux Toolkit 🧩, Node/Express 🚀, and MongoDB 🗄️. It delivers a real-time product experience with secure JWT authentication 🔐, PayPal/credit-card checkout 💳, image uploads 📸, and full admin management workflows. Designed with a scalable, production-ready architecture ideal for real-world store operations.