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AI Engineer with less than a year in Generative AI, RAG, and LLM systems.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
MCA graduate with hands-on experience building RAG pipelines, LLM multi-agents systems using LangGraph, and end-to-end GenAI applications.Proficient in Python, LangChain, OpenAI GPT, and FAISS.Seeking a fresher AI Engineer role at a product-driven startup
Bengaluru city University
Master of Computer Applications (MCA)
January 1, 2023 – January 1, 2025
AI-Powered Health Insurance Claim Validation System
January 1, 2025 – June 1, 2026
Developed an end-to-end AI system to automate insurance claim validation using RAG-based document retrieval. Reduced manual claim review time by ~70% by automating document retrieval and validation using RAG + FAISS. Improved claim verification accuracy by ~85% through LangChain + GPT-based reasoning pipeline. Implemented FAISS vector search to extract relevant policy and medical document sections for claim verification. Built a Flask-based backend to process uploaded documents and generate validation summaries. Ensured data preprocessing, embedding generation, retrieval, and reasoning pipelines worked in real time.
CodeBuddy – AI-Powered Multi-Agent Coding Assistant
January 1, 2025 – June 1, 2026
Built multi-agent system that converts natural language requests into working code in under 30 seconds using Groq LLM. Automated 100% of project scaffolding planning, file creation, and code generation via LangGraph agent workflow. Implemented a Planner-Architect-Coder agent workflow using LangGraph to automate project planning, task breakdown, and file-level code generation. Built tool-driven coding capabilities allowing agents to create, edit, and manage project files in a structured developer workflow.
Data Science & Generative AI Certification
Udemy
March 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in cutting-edge AI technologies (RAG, multi-agent systems, LLMs) and a proactive approach to learning through certifications. This aligns well with a product-driven startup culture that values innovation and continuous learning. The academic nature of the projects, however, means there's no direct evidence of collaboration within a professional team setting, which is a key aspect of cultural fit in many organizations. The breadth of skills covers core AI/ML and GenAI, indicating adaptability.
Soft Skills & Operational Fit
The candidate lists Analytical Thinking, Problem Solving, Strong Communication, Collaboration, and Stakeholder Management as soft skills. While these are valuable, there is no assessment data to validate their proficiency. The project descriptions indicate an ability to work on complex problems and integrate various technologies, suggesting good problem-solving and analytical skills. However, without specific examples or interview data, it's difficult to fully assess operational fit.