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AI Engineer with less than a year in LLM Systems & RAG Architectures
AI Engineer with end-to-end experience building production LLM-powered systems, multi-agent pipelines, and RAG architectures. Deep hands-on work with vector databases (ChromaDB, FAISS), and document intelligence - from PDF/OCR ingestion to structured data extraction. Proficient across the full AI stack: LLM APIs (OpenAI, Anthropic Claude, Mistral, Llama), orchestration frameworks (LangChain, CrewAI), and backend services (FastAPI, Flask). Strong Python fundamentals with async, REST API design, and cloud deployment on AWS. I evaluate, iterate, and ship AI products that are reliable and explainable.
Pimpri Chinchwad College of Engineering
B.Tech · Computer Engineering
November 1, 2022 – June 1, 2026
FlairMinds Software Pvt. Ltd.
AI Engineer Intern
January 1, 2026 – April 1, 2026
Pune, Maharashtra, India
AI NutriCoach - Multi-Agent RAG System
January 1, 2026 – June 1, 2026
Production multi-agent system orchestrating 5 CrewAI agents with Llama 3.2 90B via OpenRouter and GPT-40. Built a self-learning feedback loop using ChromaDB to embed user feedback and improve recommendations iteratively - hands-on with eval-driven iteration and metrics-based improvement. Designed document intelligence workflows: image ingestion, vision-based content extraction, and structured nutritional data parsing from PDFs and web sources.
View ProjectYouTubeASL - Sign Language Avatar Framework
January 1, 2026 – June 1, 2026
Built a full-stack AI pipeline converting YouTube captions into ASL avatars. Integrated Mistral 7B + LangChain for English-to-ASL-gloss translation with extensive prompt templating and iterative refinement. Implemented FAISS-based RAG for sign exemplar retrieval from How2Sign and an ETL pipeline (yt-dlp, phrase segmentation) for real-time caption processing at 30 fps.
MovieMatch - AI Recommendation Engine
January 1, 2026 – June 1, 2026
Developed an end-to-end recommendation system with hybrid ML achieving 70.2% accuracy on MovieLens (100K ratings, 9.742K movies). Built structured data pipelines with Pandas and PostgreSQL for feature extraction, TF-IDF vectorization, and cosine similarity scoring. Engineered a REST API with Flask and SQLAlchemy for real-time inference - 3-5x query performance improvement through indexing and matrix caching.
AWS Cloud Fundamentals
Amazon Web Services
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong passion for AI engineering through diverse personal projects covering multi-agent systems, sign language translation, and recommendation engines. The projects showcase a breadth of technical skills and a willingness to tackle complex problems. The internship experience, though brief, directly aligns with the target role, indicating a clear career path and focus. The combination of academic background and practical project work suggests a motivated and self-starting individual who would likely integrate well into an innovative AI team.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and iterative approach to development, including self-learning feedback loops and metrics-based improvement. This suggests a strong problem-solving mindset and an ability to adapt and refine solutions. The focus on 'eval-driven iteration' and 'shipping reliable and explainable AI products' aligns well with operational excellence in an AI engineering role.