AI Engineer with less than a year in GenAI applications, LLMs, and RAG.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Entry-level AI Engineer with hands-on experience building GenAI applications using LLMS, RAG, LangGraph, and Azure AI services. Skilled in developing scalable AI solutions, backend APIs, and intelligent workflows with a strong foundation in Python and system design
Jain University (JU)
MCA (Master of Computer Applications) · CSIT
N/A – March 1, 2026
Sree Vidyanikethan Degree College
Bsc (Bachelor of Science) · MSCS
N/A – May 1, 2022
SQL QA + RAG Chatbot
June 24, 2026 – Present
Built a GenAI-powered system that converts natural language questions into SQL queries for Excel-based datasets. Developed a RAG pipeline with document retrieval, embeddings, and citation-grounded responses. Created a FastAPI and Streamlit solution for document ingestion, conversational querying, and analytics visualization.
Enterprise Healthcare RAG Assistant (Azure + GenAI)
June 24, 2026 – Present
Built an Enterprise Healthcare RAG Assistant on Azure using Azure OpenAI (GPT-40) for answer generation and Azure AI Search for vector retrieval, orchestrated via a LangGraph 7-node workflow with confidence scoring, fallback handling, and conversational memory. Designed an Azure Blob Storage + Azure Data Factory (ADF) pipeline for healthcare document ingestion, processing PDFs with OCR and chunking content into semantic embeddings stored in Azure AI Search and Azure Database for PostgreSQL. Implemented a Neo4j AuraDB knowledge graph with medical entities and relationship mapping using Azure OpenAI entity extraction, enabling multi-hop clinical reasoning beyond traditional vector search. Developed a Doctor Portal using Streamlit, featuring citation-backed responses, confidence indicators, graph visualization, and persistent chat history, with scalable document processing powered by Azure Databricks.
Travel Planning System using Multi-Agent AI
June 24, 2026 – Present
Built a LangGraph-based multi-agent travel planner with specialized agents for flights, hotels, itineraries, and validation. Implemented a Supervisor-Agent architecture with shared state management for reliable agent coordination and decision-making. Developed and deployed a Streamlit application supporting multi-city trips, travel preferences, and automated itinerary generation.
PGDPM in Data Science
Unknown
June 1, 2026 – Present
Full Stack Development
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and initiative in the AI/GenAI domain, aligning well with an AI Engineer role. The diversity of projects (healthcare RAG, travel planner, SQL QA chatbot) shows a broad application of AI skills and a proactive learning attitude. The use of modern AI stacks (LangGraph, Azure OpenAI) indicates an alignment with current industry trends. The personal nature of all projects, while impressive for an entry-level candidate, means there's no direct evidence of working within a corporate culture or large team setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to translate complex requirements into functional AI systems. The focus on end-to-end solutions, from data ingestion to user interfaces, suggests a practical and results-oriented approach. The use of various tools and frameworks implies adaptability and a willingness to learn new technologies. However, without direct work experience, it's difficult to assess collaboration, stress handling, or direct operational fit in a team environment.