TalentedXpert -AI-Powered Talent Discovery Platform
AI / Machine Learning
Project Overview
An AI-powered talent discovery platform that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to enable semantic expert search and context-aware talent recommendations.
Tech Stack
The Problem
Traditional talent discovery often relies on exact keyword matching, making it difficult to identify relevant experts when user queries differ from the terminology used in profiles or professional information. Finding suitable candidates also requires understanding the context and intent behind a query.
The Solution
Developed an AI-powered talent discovery workflow using RAG and LangChain to support semantic expert search and context-aware recommendations. Integrated LLM workflows with backend services and REST APIs to process queries and retrieve relevant information.
Key Features
- Semantic expert search
- Context-aware talent recommendations
- LLM-powered query processing
- Retrieval-Augmented Generation (RAG) pipeline
- Backend API integration
- Context-based information retrieval
Challenges
- Improving retrieval relevance for natural-language queries
- Reducing irrelevant or insufficiently grounded LLM responses
- Selecting relevant context to support reliable answer generation
- Integrating AI workflows with backend APIs
Learnings
- Learned to build RAG workflows using LangChain and LLMs
- Improved understanding of semantic retrieval and context selection
- Learned the importance of grounding LLM responses in retrieved information
- Gained experience integrating AI pipelines with backend services
Case Study
TalentedXpert — AI-Powered Talent Discovery
Overview
TalentedXpert is an AI-powered talent discovery platform designed to improve expert search and talent recommendations using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
My contribution focused on AI and backend development, including LLM workflows, information retrieval, and API integration.
Problem
Traditional keyword-based search can miss relevant experts when users describe their requirements differently from the terminology found in professional profiles. Understanding the intent behind a query and retrieving relevant supporting information are essential to producing useful recommendations.
Solution
I developed RAG workflows using LangChain and OpenAI models to support semantic expert search and context-aware talent recommendations. The system combines information retrieval with LLM-based processing to generate responses informed by relevant context.
Technical Implementation
Integrated OpenAI LLMs into AI-powered query-processing workflows.
- Developed RAG pipelines using LangChain.
- Worked on semantic expert search and context-aware talent recommendations.
- Integrated AI functionality with backend services and REST APIs.
- Worked with Python, FastAPI, NestJS, and MySQL in the backend stack.
Challenges and Improvements
One challenge was handling queries for which the retrieved information was incomplete or insufficiently relevant, leading to responses that were not adequately grounded in the available context.
I refined retrieval workflows, context selection, and prompt structure to improve the relevance of supporting information and help keep generated responses grounded in retrieved content.
Key Takeaways
This project strengthened my practical experience in LLM application development, RAG architecture, semantic retrieval, prompt design, and backend integration. It also reinforced that reliable AI applications depend on retrieval quality and context management, not just the underlying language model.
Technologies
Python, OpenAI API, LangChain, RAG, FastAPI, NestJS, MySQL, REST APIs.