AI Engineer with 1+ years in Data Science & LLMs
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Passionate engineer interested in Data Science, Artificial Intelligence, and Machine Learning, with hands-on experience developing projects involving LLMs, RAG pipelines, Agentic systems, and data-driven applications.
Sir M Visvesvaraya Institute of Technology
B.E · Computer Science & Engineering
December 1, 2022 – Present
Glacis, US-based Startup for AI in Supply Chain
Agentic AI Engineering Intern
April 1, 2026 – Present
India
Skill Intern Pvt Ltd
Artificial Intelligence Intern
February 1, 2024 – April 1, 2024
India
SMVIT, Bangalore
Team Manager, TechHub Community (Coding Club)
December 1, 2023 – December 1, 2024
Bengaluru, Karnataka, India
Structured Questionnaire Answering System
February 1, 2026 – March 1, 2026
Built a Hybrid RAG system that answers structured Excel questionnaire queries using context retrieved from uploaded PDF documents. Implemented dual retrieval combining Pinecone vector similarity search and BM25 keyword search to improve accuracy for complex queries and structured document content. Developed and deployed a Streamlit application with Supabase authentication for user sign-up and login, PDF ingestion with SHA-256 deduplication, secure storage, and evidence-based answer verification for human-in-the-loop review.
CSRD AI Agent - Agentic ESG Data Extraction System
December 1, 2025 – January 1, 2026
Built a LangGraph-orchestrated Agentic RAG pipeline to extract structured ESG metrics (Scope emissions, gender pay gap, etc.) from 1000+ page CSRD annual reports. Designed a Router-Retriever architecture with heuristic page scoring, reducing LLM token usage by ~95% while enforcing strict JSON schema outputs via GPT-40 and Pydantic. Implemented validation and audit logic with confidence scoring, unit checks, and page-level citations, exporting compliance-ready data to CSV / SQLite for further analysis.
Resume Screening AI Agent
November 1, 2025 – December 1, 2025
Built an Agentic AI resume screening system to automate job description parsing, resume extraction, scoring, and candidate ranking. Developed a multi-factor scoring engine using OpenAI embeddings to evaluate skills, semantic fit, and experience, improving JD-candidate alignment. Designed and deployed a Streamlit dashboard with LLM-generated candidate rationales, blind resume screening, automated PDF reports, real-time scoring, and audit-ready logging on Streamlit Community Cloud.
VAKIL – Virtual Assistant for Knowledge in Indian Law
July 1, 2025 – November 1, 2025
Fine-tuned Microsoft Phi-3 Mini 4K Instruct (3.8B) model using LoRA on 76k Indian legal instruction-response dataset (Indian Laws, constitutional acts, statutory sections) training for 18 hours on RunPod RTX A6000 GPU, improving domain-specific response quality. Conducted intrinsic and extrinsic evaluations, published the fine-tuned model on Hugging Face Hub, and deployed via VLLM serverless with secure API endpoints for low-latency inference. Built a RAG pipeline using FAISS vector similarity search over dense embeddings for context-aware legal question answering.
Ola Bike Ride Demand Forecasting Using ML
October 1, 2024 – December 1, 2024
Developed and optimized a Regression-based ML model using LightGBM after evaluating multiple algorithms, achieving 93%+ prediction accuracy. Built a scalable Flask web app with an Ola-themed UI that delivers real-time ride demand forecasts. Integrated Google Calendar and OpenWeather APIs for context-aware predictions based on user-input date, time, and location.
5-Day AI Agents Intensive course
Kaggle & Google
December 1, 2025 – Present
5-Day Gen AI Intensive Course
Kaggle & Google
April 1, 2025 – Present
DSA Mentor - CSOC 2024
TechHub Community
August 1, 2024 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from legal AI assistants to ESG data extraction and resume screening, indicates a broad interest in applying AI across different sectors. Their involvement in a coding club as a DSA mentor and leading technical events suggests a commitment to community and continuous learning, which aligns well with a collaborative and growth-oriented culture. The candidate's focus on building practical, deployable solutions with clear business impact (e.g., automating workflows, reducing manual effort) demonstrates a results-driven approach.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in optimizing LLM token usage and improving retrieval accuracy. Their experience as a DSA mentor and team manager indicates leadership potential and a collaborative mindset. The detailed project descriptions suggest good communication of technical concepts. The candidate's proactive engagement in building complex AI systems for various domains (legal, ESG, HR) shows initiative and a strong work ethic.