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AI Engineer with less than a year in Generative AI, LLMs, & MLOps.
Results-driven AI/ML Engineer specializing in Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) systems, with production experience shipping Machine Learning models, NLP pipelines, and Dockerized REST APIs on AWS EC2. Owned the end-to-end ML lifecycle for an HR classification system (89% accuracy, 0.87 F1-score) and independently architected DocuMind AI, a Hybrid RAG Q&A platform built with LangChain, FAISS, and Llama 3 achieving sub-300ms inference latency. Proficient in PyTorch, TensorFlow, Hugging Face Transformers, Prompt Engineering, Agentic AI, Vector Databases, and MLOps practices (CI/CD, containerization, experiment tracking with MLflow). Seeking to leverage strong fundamentals in NLP, LLM application development, and scalable ML infrastructure as an AI Engineer / Generative AI Engineer.
VSM's Somashekhar R Kothiwale Institute of Technology, Nipani
B.E. · Electronics & Communication Engineering
August 1, 2021 – June 30, 2025
Smt V P Hanchinmani Independent Science P U College, Dharwad
PUC · Science
June 1, 2018 – May 31, 2020
Gtechnohubb Solutions Pvt Ltd
AI Engineer Intern
September 1, 2025 – March 1, 2026
Bengaluru, Karnataka, India
DocuMind AI – Hybrid RAG & Web Intelligence Assistant
June 23, 2026 – Present
Architected a Hybrid Retrieval-Augmented Generation (RAG) system end-to-end: an intelligent document assistant supporting PDF, DOCX, and TXT ingestion, semantic search, and conversational Q&A using LangChain, FAISS, Sentence Transformers, FastAPI, React.js, and a locally hosted Llama 3 model via Ollama, achieving under 300ms average query latency. Designed a confidence-based retrieval engine using FAISS similarity scores that dynamically routes queries between Document Mode, Web Search Mode, and Hybrid Mode, ensuring accurate responses even when information is absent from uploaded documents. Engineered a web intelligence layer with DuckDuckGo-powered retrieval fallback, contextual result filtering, source attribution, and prompt orchestration to unify document knowledge with real-time web information in a single answer-generation pipeline. Optimized retrieval quality through chunking, embedding generation, vector indexing, top-k retrieval, and structured, source-aware prompting, measurably reducing hallucinations and improving answer relevance across document and web-based queries.
View ProjectEmail Spam Detector
June 23, 2026 – Present
Sole author: benchmarked Naive Bayes, Logistic Regression, and a 3-layer Artificial Neural Network (ANN) on the UCI SMS Spam dataset (5,572 samples, 80/20 split); best model achieved 97.4% accuracy and 0.96 F1-score on the held-out test set. Built and deployed a full NLP pipeline (tokenization, stopword removal, lemmatization, TF-IDF with 10K features) as a Dockerized Flask application with a live /predict REST endpoint hosted on Render.
View ProjectThe candidate scored 94% on the 'Data Scientist — Artificial Intelligence' exam, indicating a very strong grasp of the core concepts and skills required for the role.
Strengths
Limitations
Cultural Fit Analysis
The candidate's projects demonstrate a strong initiative and passion for AI/ML, particularly in Generative AI and NLP, which aligns well with an AI Engineer role. The diversity of projects (RAG system, spam detector, HR classification) showcases a broad interest and applicability of AI skills. The MLOps experience indicates an understanding of production-ready systems, which is crucial for cultural fit in a professional engineering environment.
Soft Skills & Operational Fit
The candidate's resume highlights a results-driven approach and ownership of ML lifecycle components. The psychometric test score of 374/500 suggests a reasonable work attitude and stress handling, though specific details are limited. The experience in an intern role with senior engineer guidance indicates an ability to collaborate and learn within a team structure.