AI Engineer with less than a year in LLM pipelines, RAG systems, and prompt-driven automation.
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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.