Automated Employee Profiling & Resume Processing Pipeline

Developed a GenAI-powered LLM module using LangChain, integrated with Llama 3.1 (8B) via Ollama, to automate the extraction and structuring of employee information from resumes, reducing HR lookup time by 80% and improving HR team efficiency in candidate evaluation.

Developed the Top 5 Skills Identification Module using a Llama 3.1-based solution, which helped increase skill categorization accuracy by 89% over manual keyword-based methods, enabling HR teams to better assess candidate fit for roles.

Led architectural decisions within the scope of LLM-related modules with LangChain, focusing on resume extraction and skill identification

Utilized hybrid prompting techniques (Instruction-based, Constraint-based, Structured JSON Output) for extracting skills, experience, and project details from unstructured resumes, ensuring structured, reliable output in the required JSON format.

Collaborated closely with HR teams to understand their requirements and refine the resume extraction and skills identification process. Worked cross-functionally with data analysts and Python developers to ensure seamless integration of LLM-based modules.