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AI Engineer with less than a year in AI, ML, and LLM Development.
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Assessing your cultural and operational fit
Highly motivated and results-oriented Junior AI Engineer with 3 months of hands-on experience in developing and deploying AI-driven solutions. Proficient in machine learning, deep learning, large language models (LLMs), and Retrieval-Augmented Generation (RAG). Demonstrated ability to build full-stack AI platforms, anomaly detection systems, and conversational AI assistants, delivering measurable improvements in accuracy and efficiency. Eager to contribute expertise in AI agent design, API integration, and cloud computing to innovative projects.
RNS PU College
Pre University College · PCMC
June 1, 2020 – May 31, 2022
Don Bosco Institute of Technology
B.E. · Artificial Intelligence and Data Science
N/A – June 30, 2026
Unified Mentor
Data Science intern
February 1, 2025 – April 1, 2025
India
MediMind - RAG-Enhanced LLM for Smarter Clinical Decisions
June 1, 2026 – Present
Developed a LangChain-powered Retrieval-Augmented Generation (RAG) system using HuggingFace embeddings and vector databases, achieving a 32% boost in retrieval precision for clinical document search. Deployed MediMind on FastAPI with optimized LLM pipelines (LangChain agents + prompt orchestration), improving medical QA response accuracy to 87% and reducing latency by 28% in production.
AI ARCHITECT – Smart AI-Driven Interior & Home Design Platform
June 1, 2026 – Present
Built the full-stack architecture using Next.js (React), Tailwind CSS, Python + FastAPI, and Supabase (PostgreSQL, Auth, Realtime, Storage), integrating Large Language Models (LLaMA, Qwen, Gemini) and generative design models to power conversational guidance, architectural reasoning, and design explanations across the platform. Designed and implemented AI-driven planning features, including automated interior, ceiling, and floor-plan generation, layout optimization, Vastu compliance validation, cost and material estimation, furniture recommendations, and contractor coordination, enabling a unified design-to-execution workflow.
Financial AI Assistant (FinTech)
June 1, 2026 – Present
Implemented a conversational AI assistant using LLMs (OpenAI, Gemini) and predictive analytics, providing fraud detection, stock insights, debt management, and budgeting, and reducing manual analysis time by 60% Developed using FastAPI, Scikit-learn, Pinecone, and LangChain, empowering users with real-time portfolio tracking and smarter financial decisions—achieving 92% precision in fraud detection cases.
AI Fashion | E-Commerce Platform
June 1, 2026 – Present
Built a full-stack AI fashion ecommerce platform using React, FastAPI, and Replicate, integrating virtual try-on, visual search, and a multimodal newsfeed. Increased session duration by 3x and click-through rate by 28% Enhanced personalized shopping through multimodal LLMs (OpenRouter, Gemini), recommendation APIs, and real-time trend feeds, resulting in ~35% improvement in product discovery based on A/B testing.
Machine Learning with Python
Simplilearn
June 1, 2026 – Present
Python Programming bootcamp, Real World Machine Learning Applications
Udemy
June 1, 2026 – Present
AI-ARCHITECT- Smart AI-Driven Interior & Home Design Platform
IEEE
November 1, 2025 – Present
Google Skill Badge for building and deploying Generative AI apps on Google Cloud
July 1, 2025 – Present
Cultural Fit Analysis
The candidate's diverse portfolio of personal projects (AI Architect, MediMind, Financial AI Assistant, AI Fashion) showcases a broad interest in applying AI across different domains, indicating adaptability and a willingness to explore new challenges. The focus on practical, impact-driven solutions (e.g., reducing manual analysis time, improving accuracy) aligns with a results-oriented culture. The proactive pursuit of certifications and a research paper suggests a strong learning mindset and dedication, which are positive indicators for cultural fit in an innovative environment.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving skills, evidenced by developing solutions for fraud detection, clinical decision support, and automated design. The ability to integrate various technologies (LLMs, vector databases, front-end frameworks) suggests strong operational fit for end-to-end AI product development. The personal projects demonstrate initiative and a self-starter attitude, crucial for an AI Engineer role.