ASSOCIATE AI ENGINEER - OAK STREET TECHNOLOGIES
(2025-08)
Tech Stack: Python, FastAPI, OpenAI API (GPT-4.1 Mini & GPT-4.x), LangChain, LangGraph, LangSmith, Hugging Face Transformers (DistilBERT), Qdrant, ChromaDB, Weaviate, Azure Key Vault, SQL
- Engineered a production-grade multi-agent AI platform using FastAPI, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), and OpenAI models to support enterprise-scale conversational AI solutions for 1,000+ expected users.
- Designed and developed 10+ FastAPI backend services and AI endpoints powering enterprise conversational workflows, intelligent routing, document retrieval, and NL-to-SQL capabilities.
- Architected a supervisor-agent workflow using LangGraph State Graphs with specialized agents responsible for query understanding, retrieval, SQL generation, report interpretation, validation, and response generation.
- Implemented tool-calling workflows, conversational memory, checkpointing, prompt templates, and human-in-the-loop interactions to improve workflow reliability and maintain conversational context.
- Integrated more than 10 enterprise APIs to enable secure, real-time retrieval of business information across multiple internal systems.
- Designed and implemented an enterprise NL-to-SQL solution capable of translating natural language business questions into schema-aware SQL queries across approximately 70 relational database tables.
- Developed a retrieval-enhanced SQL generation and validation pipeline using OpenAI models, semantic retrieval, metadata-aware context selection, and agent orchestration.
- Achieved approximately 97% SQL generation accuracy through schema-aware prompting, contextual retrieval, and automated query validation.
- Automated reporting workflows across more than 90 enterprise reports, reducing manual reporting effort by an estimated 88–93%.
- Enabled conversational access to reports containing over 80,000 records, reducing information retrieval time from minutes to seconds.
- Optimized LLM inference costs by strategically utilizing GPT-4.1 Mini for cost-sensitive workloads while dynamically leveraging larger OpenAI models for complex reasoning tasks, balancing operational cost with response quality.
- Applied prompt engineering techniques to improve response consistency, retrieval quality, and structured output generation across enterprise AI workflows.
- Utilized LangSmith for observability, request tracing, debugging, and monitoring of LLM execution paths to improve system reliability and simplify troubleshooting.
- Fine-tuned a Hugging Face DistilBERT transformer model for enterprise intent classification using approximately 7,000 proprietary natural language queries spanning multiple business intent categories.
- Improved intent recognition accuracy to approximately 80%, enabling intelligent request routing and more accurate agent selection within LangGraph orchestration workflows.
- Developed an AI-powered enterprise search platform using semantic search, hybrid retrieval, embedding-based search, and metadata filtering across Qdrant, ChromaDB, and Weaviate vector databases.
- Evaluated multiple vector database technologies to compare retrieval quality, scalability, indexing performance, and enterprise search effectiveness before selecting solutions for production use.
- Integrated Azure Key Vault to centralize secure management of API keys, database credentials, and environment-specific configuration across staging, UAT, and production environments.
- Applied object-oriented software engineering principles to develop reusable components supporting secure credential management and enterprise AI services.
- Performed feature validation and functional testing for newly implemented capabilities before integration into the production codebase.
WEB DEVELOPER INTERN - 9D TECHNOLOGIES
(2024-06 - 2024-08)
Tech Stack: JavaScript, React.js, Node.js, Express.js, MongoDB, HTML, CSS
- Developed reusable frontend components and interactive user interfaces using JavaScript, HTML, and CSS for internal business applications.
- Collaborated on the development of a MERN-stack Online Quran Learning platform, contributing to both frontend and backend application features.
