AI Engineer with less than a year in RAG pipelines, multi-agent systems, and LLM evaluation framewor
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Assessing your cultural and operational fit
AI/LLM Engineer fresher with hands-on experience building RAG pipelines, multi-agent systems, and LLM evaluation frameworks using LangChain, LangGraph, and LlamaIndex. Skilled in designing modular AI architectures with FastAPI backends and Streamlit UIs, deploying production-grade Generative AI products. Strong communicator with proven ability to collaborate cross-functionally and deliver results under pressure.
Acropolis Institute of Technology and Research, Indore
Bachelor of Science · Information Technology
August 1, 2021 – June 30, 2025
The St Peters Higher Secondary School, Indore
Class 10th & 12th
June 1, 2017 – May 31, 2020
Local AI Assistant with Offline LLM
January 1, 2026 – June 1, 2026
Designing a privacy-first AI assistant running entirely on local LLMs via Ollama, eliminating dependency on external API services and enabling offline model deployment for sensitive enterprise use cases. Implementing tool-calling workflows and conversational memory using LangGraph for stateful multi-turn interactions with cloud computing-independent architecture. Integrating MCP (Model Context Protocol) for standardized agent-tool communication and extensible tool registration, with attention to data preprocessing pipelines for diverse local document formats.
View ProjectMulti-Agent AI Trip Planner
January 1, 2026 – June 1, 2026
Built a multi-agent travel planning system using LangGraph with 4 specialized agents, conditional routing, and shared state management, reducing itinerary generation time by ~40% vs. single-agent approaches. Integrated 5 LLM-powered tools for dynamic itinerary generation, search, and preference-aware travel recommendations via prompt engineering, improving recommendation relevance through iterative hyperparameter tuning of temperature and top-p settings. Developed a FastAPI backend exposing agentic API endpoints and a Streamlit frontend delivering an interactive conversational interface with sub-2-second response times through performance optimization of the inference pipeline.
View ProjectRAG Pipeline with RAGAS Evaluation
January 1, 2026 – June 1, 2026
Architected an end-to-end RAG pipeline ingesting 1 PDF document with semantic chunking, embedding generation, and vector retrieval using LlamaIndex, improving retrieval accuracy by an estimated 20% over keyword-based search. Integrated RAGAS evaluation framework measuring faithfulness, answer relevancy, context precision, and context recall across a 30-sample evaluation dataset, achieving 85%+ average scores across all metrics. Designed a modular src/ architecture separating ingestion, chunking, embeddings, retrieval, and evaluation modules, enabling reproducible benchmarking and collaborative extensibility for future team contributions. Containerized the full pipeline with Docker for environment-independent execution across development and production environments.
View ProjectDBMS Certification
NPTEL
January 1, 2025 – Present
AWS Certified AI Practitioner (AIF-C01)
AWS
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's project diversity, focusing on privacy-first AI, multi-agent systems, and RAG pipelines, aligns well with an innovative and problem-solving culture. The AWS certification and active participation in a cloud enthusiasts club indicate a proactive learning attitude and engagement with the broader tech community. However, the lack of professional work experience might indicate a need for mentorship in a fast-paced corporate environment.
Soft Skills & Operational Fit
The candidate's involvement in leading the AWS Cloud Enthusiasts Club and securing a top 10 position in a college hackathon suggests strong communication, leadership, and teamwork skills. The ability to deliver cloud-based solutions under pressure indicates good operational fit and stress handling. However, without specific psychometric test results, a deeper assessment of work attitude and team collaboration is limited.