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Assessing your cultural and operational fit
LLM-Output-Validator-Schema-Enforcer-1.0
May 28, 2026 – Present
LLM-Output-Validator-Schema-Enforcer-1.0 — GitHub repository
View ProjectTRAVEL_AGENT_ADK
March 25, 2026 – Present
Multi-agent AI travel planner built with Google ADK parallel specialist agents for transport, hotels & sightseeing collaborate via A2A protocol to generate structured, search-grounded trip plans from a single query.
View ProjectSemantic-Alignment-in-Churn-Prediction-Model
March 5, 2026 – Present
An ML case study on **Semantic Misalignment in churn prediction models**. The project introduces a hybrid **ML + Semantic Alignment layer** that corrects logically inconsistent predictions and demonstrates a reusable framework for improving real-world ML systems. Tech: Python • Scikit-learn • Gradio • Docker • AWS ECS
View ProjectVectorization-RAG-
February 11, 2026 – Present
Simple Retrieval-Augmented Generation (RAG) A beginner-friendly implementation of Retrieval-Augmented Generation using LangChain and Chroma Vector Store, focused on basic document ingestion, text embedding, vector indexing, and similarity-based retrieval for learning core RAG concepts.
View Projectvaulted-cloud-storage
February 8, 2026 – Present
A full-stack cloud storage application built with Next.js and Appwrite, focused on hands-on learning of authentication, BaaS workflows, and secure file management with a clean, production-ready interface.
View ProjectKnowledge-Bank-Chatbot
February 7, 2026 – Present
"Knowledge Bank Chatbot" A modular RAG-based chatbot that combines a web interface, document retrieval, and a Supabase backend to deliver context-aware conversations from a custom knowledge base.
View Projectcode-review-agent-langgraph
February 7, 2026 – Present
"CODE - REVIEW - AGENT - LANGGRAPH" An automated code review agent built with LangGraph and a local Ollama LLM that analyzes repositories, identifies issues, explains tradeoffs, and generates structured review reports (without relying on paid APIs.)
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong interest in AI/ML, particularly in areas like multi-agent systems, RAG, and LLM output validation, which aligns well with a Data Scientist role. The diversity of projects, from AI planners to full-stack applications, demonstrates a broad technical curiosity and willingness to learn new technologies. However, the lack of professional experience or team-based projects makes it challenging to fully assess cultural fit in a collaborative work environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-directed learning approach, particularly in exploring advanced AI concepts and full-stack development. The focus on building functional applications suggests a practical, problem-solving mindset. However, without psychometric or English test results, it's difficult to assess communication clarity, work attitude, stress handling, or team collaboration skills.