Swarup Kusalkar
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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 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.