Help & Supportcareers@gravityer.com
Generative AI Engineer with less than a year in LLMs & NLP
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML Engineer with hands-on expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP). Proficient in designing end-to-end LLM pipelines, vector embedding architectures, and AI agents using LangChain, FastAPI, and Hugging Face. Adept at prompt engineering, semantic search, and deploying intelligent automation systems at scale. Passionate about real-world impact through AI-driven solutions.
Sri Venkateswara University
B.Tech · Computer Science & Engineering
August 1, 2022 – June 30, 2026
Coincent.ai (E-Cell IIT Madras)
Data Science & AI Intern
February 1, 2024 – March 1, 2024
India
Equity Research Dashboard — AI-Powered Financial Intelligence Platform
January 1, 2026 – Present
Built a RAG-powered financial intelligence platform using LangChain, Google Gemini, and FAISS for context-aware Q&A over financial reports. Designed semantic retrieval pipelines with vector embeddings enabling low-latency search across large financial datasets. Deployed an interactive Streamlit dashboard delivering source-grounded LLM responses for investment research workflows.
Multi-Document RAG Assistant — Generative AI Knowledge Retrieval System
January 1, 2026 – Present
Developed a multi-document RAG assistant supporting PDF, DOCX, TXT, CSV, XLSX, and URL ingestion using LangChain and FAISS. Implemented chunking, embedding generation, vector indexing, semantic retrieval, and citation-aware responses for accurate knowledge extraction. Integrated Llama 3.3 70B via Groq API with intelligent routing between document-grounded and general-purpose LLM conversations.
Job Description Simplifier — AI-Powered Recruitment Intelligence Tool
January 1, 2026 – Present
Built an end-to-end NLP application powered by Llama 3.3 70B via LangChain and advanced prompt engineering to parse and structure complex job descriptions into actionable summaries. Developed a web-scraping and content-preprocessing pipeline for automated large-scale collection and normalisation of job posting data, demonstrating ETL and data engineering proficiency. Implemented LLM-based information extraction to surface key skills, roles, and responsibilities from unstructured text; deployed Streamlit UI for rapid role-fit assessment.
Google AI Essentials
Coursera
June 1, 2026 – Present
Artificial Intelligence with Python
E-Cell IIT Madras
June 1, 2026 – Present
RescueGrid: A Real-Time Bio-Logistics and Emergency Donor Matching System
AIJFR Journal
January 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to diverse domains (knowledge retrieval, recruitment, finance, bio-logistics), indicating adaptability and a broad perspective. The involvement in hackathons and research suggests a collaborative spirit and a drive for continuous learning. However, the limited professional experience (one short internship) means cultural fit is largely inferred from personal projects and academic involvement, which may not fully reflect workplace dynamics.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving attitude, with a focus on delivering practical AI solutions. The internship experience highlights an ability to contribute to ETL pipelines and apply analytical techniques. The psychometric test score (310/500) suggests average performance in areas like logical reasoning and work attitude, which might require further validation during interviews.