Vishal Gaikwad

AI Engineer · Agentic AI, LLMs & RAG

Pune, Maharashtra, India

@vishal_gaikwad

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🎓 BS in Data Science and Applications at IIT Madras · Graduating 2027

About

AI Engineer focused on building agentic AI systems, LLM applications, RAG pipelines, and AI evaluation systems. Experienced with multi-agent orchestration, tool calling, memory systems, vector search, and backend development using Python, FastAPI, LangChain, and LangGraph.

Education

BS in Data Science and Applications

IIT Madras

Data Science and Applications · Sep 2023 – Dec 2027
Diploma in Data Science

IIT Madras

Data Science · Jan 2025 – Apr 2026

Skills

<3 months

AWS RAG MCP Ragas Python CrewAI FastAPI PyTorch Power BI Chromadb LangChain LangGraph LangSmith Databricks Embeddings SQLAlchemy scikit-learn Vector Search Model Evaluation Transfer Learning Prompt Engineering Feature Engineering Multi-Agent Systems API Integration Understanding CNN (Convolutional Neural Network)

Tools / apps / platforms

6 to 12 months

Git SQLite PostgreSQL

3 to 6 months

Redis Docker

Languages

Fluent

English Speak · Read

Projects

Compass — Proactive Growth Opportunity Engine

Anthropic SDK, FastAPI, PostgreSQL, DuckDB, APScheduler

Built an agentic analytics system with scheduled analysis and chat-based interaction connected to a shared analytics core. Developed parallel detectors for anomalies, high-value segments, and historical patterns using validated read-only SQL queries. Added SQL injection protection, PII masking, human-in-the-loop controls, and CI-based evaluation for precision, recall, and answer faithfulness.
Image Memory — Multimodal AI Memory System

FastAPI, Streamlit, OpenRouter, SQLite, sqlite-vec, OpenCV, Python

Designed a modular FastAPI system for image ingestion, semantic indexing, vector retrieval, and smart collections. Built a multimodal pipeline that generates image captions, metadata, semantic embeddings, and facial embeddings. Implemented semantic search, reverse image search, duplicate detection, and related-image recommendations with an interactive Streamlit dashboard.

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