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AI Engineer with less than a year in Machine Learning & GenAI Development.
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Machine Learning and GenAI Engineer with hands-on experience building end-to-end ML pipelines, production-ready RAG systems, and LLM-powered applications. Skilled in modular architectures with FastAPI deployed on AWS EC2 using Docker. Experienced in NLP, prompt engineering, and ML model training and deployment. Seeking ML Engineer | Generative AI Engineer | AI Backend Developer roles.
APJ Abdul Kalam Technological University
B.Tech · Computer Science Engineering
August 1, 2018 – June 30, 2022
State Board of Technical Education, Kerala
Diploma · Engineering
August 1, 2016 – June 30, 2019
G.H.S.S Niramruthur
Higher Secondary (+2) · Computer Science
June 1, 2014 – May 31, 2016
U Digital Content Pvt. Ltd. (Arre)
Front-End Developer Intern (Flutter)
April 1, 2025 – July 1, 2025
India
GENAI DOCQ – AI-Powered Document Q&A System (RAG)
January 1, 2024 – January 1, 2025
Built a production-ready RAG system using Python, LangChain, FAISS, and LLM APIs with FastAPI endpoints; 90% response relevance across 100+ page documents. Implemented full document ingestion, vector embeddings, and modular backend for scalable responses via prompt engineering.
View ProjectAI ID FRAUD DETECTION - Document Forgery Analysis Tool
January 1, 2024 – January 1, 2025
Built fraud detection system using ELA, Tesseract OCR, and LLAMA 3 via Ollama generating structured risk reports with 85% detection accuracy. Exposed via FastAPI + Streamlit; combined pixel-tampering detection, OCR, and LangChain LLM reasoning in a modular pipeline.
View ProjectOPEN - AI-Powered Skill-Based Hiring Platform (Full-Stack)
January 1, 2024 – January 1, 2025
Architected full-stack hiring platform using Flutter, FastAPI, and PostgreSQL with JWT auth; companies post real tasks and evaluate candidates on demonstrated skills. Built AI RAG chat using Hugging Face sentence-transformers, pgvector semantic search, and OpenRouter LLM for context-aware insights. Integrated Cloudinary; dual-feed architecture with AI companion.
View ProjectNETWORKML - Modular Machine Learning Pipeline
January 1, 2023 – January 1, 2024
Built a production-ready end-to-end ML pipeline using Python, Scikit-learn, XGBoost, and CatBoost with modular components and logging, cutting iteration time by 40%. Trained and compared 5+ regression models achieving 94% accuracy; saved best-performing artifacts enabling reproducible experimentation.
View ProjectSCORE PREDICTION – ML Web Application
January 1, 2023 – January 1, 2024
Designed and automated a full ML workflow in Python using Scikit-learn, XGBoost, and CatBoost; streamlined data ingestion, preprocessing, and model training with reusable components, reducing manual effort by 60%. Evaluated and compared multiple regression models and integrated the best-performing model into a Flask-based application for prediction and testing purposes.
View ProjectCultural Fit Analysis
The candidate's portfolio is heavily focused on personal projects, demonstrating strong self-motivation and a passion for AI/ML. The diversity of projects, ranging from traditional ML pipelines to advanced RAG systems and full-stack AI platforms, indicates a broad interest and willingness to explore different facets of AI engineering. The target role of 'AI Engineer' aligns well with the candidate's demonstrated skills and project experience, suggesting a good cultural fit for a role requiring innovation and hands-on development in AI.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-component systems and a focus on modularity and efficiency (e.g., 'cutting iteration time by 40%', 'reducing manual effort by 60%'). Collaboration in an Agile environment is mentioned in the internship, suggesting an understanding of team workflows. The projects also show initiative and problem-solving skills in developing practical AI solutions.