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AI Engineer with less than a year in LLM pipelines, RAG systems, and prompt-driven automation.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Generative AI Engineer (fresher) with hands-on experience building production-grade LLM pipelines, RAG systems, and prompt-driven automation. Proficient in Python, LangChain, Vector Databases (ChromaDB), FastAPI-ready workflow design, and Docker basics. Completed two AI/ML internships delivering real-world solutions in code intelligence, document understanding, and automated generation. Strong foundation in AI/ML fundamentals, eager to contribute to scalable GenAI products.
Malla Reddy College of Engineering and Technology
B.Tech · Computer Science & Engineering (AI/ML Specialisation)
August 1, 2022 – June 30, 2026
Rishi High School
SSC
June 1, 2022 – May 31, 2022
SR Junior College for Girls
Intermediate · MPC
June 1, 2020 – May 31, 2022
Infosys
AI / Machine Learning Intern
October 1, 2025 – December 31, 2025
India
Skillible
Generative AI Intern
June 1, 2024 – August 31, 2024
India
CodeGenie - AI Explainer & Code Generator
June 1, 2026 – Present
Built a privacy-focused local AI system combining OCR, document understanding, and LLM-powered code generation — integrating Tesseract OCR and PDFPlumber for multi-format document ingestion. Implemented Ollama-based local inference to process images and PDFs, generate contextual code explanations, and automate summarization via a clean Streamlit UI.
Multi-Source RAG Research Assistant
June 1, 2026 – Present
Architected a production-style RAG pipeline ingesting PDFs, web pages, DOCX, TXT, and YouTube transcripts into ChromaDB vector store with MMR retrieval and source-attributed responses - showcasing end-to-end RAG and Vector Database expertise. Deployed local LLM inference via Ollama (Llama 3.2) with an LLM-as-judge evaluation pipeline achieving faithfulness 0.62, answer relevance 0.74, and context quality 0.68, demonstrating rigorous AI/ML evaluation methodology. Exposed the pipeline through a Streamlit UI, making it accessible as a FastAPI-ready web service; containerisation-ready design aligned with Docker deployment requirements.
Machine Learning Foundations
AWS
June 1, 2026 – Present
Solve It With SQL
Oracle Academy
June 1, 2026 – Present
Linear Regression with Python
Coursera
June 1, 2026 – Present
Deep Learning for Developers
Infosys
June 1, 2026 – Present
Empower Second Edition Advanced
Cambridge
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships are highly aligned with an AI Engineer role, demonstrating a clear passion and focus on the field. The diversity of projects (RAG, code generation, document understanding) and the use of various tools (LangChain, ChromaDB, Ollama, Streamlit, FastAPI, Docker) indicate a broad technical curiosity and willingness to explore different facets of AI/ML. The certifications further support a proactive learning attitude. This strong alignment with the target role and continuous learning mindset suggests a good cultural fit for a dynamic AI engineering team.
Soft Skills & Operational Fit
The candidate's project descriptions highlight an ability to architect and implement complex AI systems, suggesting strong problem-solving and execution skills. The focus on 'production-style' pipelines and 'rigorous AI/ML evaluation methodology' indicates an attention to detail and quality. The internships suggest an ability to learn and apply new concepts in a professional setting. However, without direct behavioral assessment, further evaluation of teamwork, communication, and adaptability is needed.