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AI Engineer with less than a year in Python, ML, and NLP
AI and Machine Learning Engineer with strong experience in building data-driven applications and intelligent systems using Python. Skilled in machine learning, predictive analytics, pattern recognition, neural networks, and statistical analysis, along with data preprocessing, feature engineering, and model development. Experienced in analyzing large datasets to extract meaningful insights and developing scalable predictive models for real-world applications, with proficiency in backend integration using FastAPI and MySQL and hands-on experience deploying ML-powered web applications using Streamlit. Developed AI-driven chatbots and recommendation systems using modern architectures including Retrieval-Augmented Generation (RAG), and Demonstrated expertise in solving complex problems, optimizing model performance, and delivering high-impact, production-ready AI solutions within collaborative environments.
Periyar University
Master of Science (M.Sc.) · Data Science
August 1, 2023 – April 1, 2025
Loyola College of Arts and Science
Bachelor of Science (B.Sc.) · Computer Science
June 1, 2020 – April 1, 2023
Sourcesys Technologies Private Limited
Trainee - AI Full stack (Full-time)
September 1, 2025 – February 1, 2026
India
Boston Training Academy
AI Developer Intern (Full-time)
January 1, 2025 – April 1, 2025
India
NexaBank - AI Loan Eligibility Agent
April 1, 2023 – August 1, 2023
Built a conversational AI loan eligibility agent using LangGraph to orchestrate a 6-node state machine that collects applicant details through natural multi-turn chat, validates PAN/Aadhaar/age, and evaluates eligibility via a deterministic Python rule engine. Integrated Groq's LLaMA 3.3 70B with a TF-IDF RAG pipeline that chunks a bank policy document, retrieves relevant sections using cosine similarity, and injects them into LLM prompts so approval/rejection letters cite real bank policy text. Connected a FastAPI backend and MySQL database via SQLAlchemy ORM with a Flask-served HTML/CSS frontend featuring a chat interface and an admin dashboard with real-time Chart.js analytics and application management.
IPL Playing XI Prediction System
April 1, 2023 – August 1, 2023
Developed an ML-based predictive system to forecast IPL Playing XI for 2025 matches by processing ball-by-ball JSON data from Cricsheet (2023-2024 seasons). Performed feature engineering on player strike rates, bowling economy, and venue trends, implementing a Random Forest Classifier to achieve 81% accuracy. Integrated a Django backend with MySQL to store structured player stats and serve predictions, optimizing the data pipeline for better performance.
Demand & Sales Forecasting System
April 1, 2023 – August 1, 2023
Built a predictive analytics model for demand and sales forecasting model using a saree sales dataset with features like fabric type and price range, and performed week-wise aggregation based on client requirements. Implemented multiple models including ARIMA, SARIMAX, LightGBM, and XGBoost to predict weekly sales quantity and demand trends. Selected XGBoost as the final model, achieving 24% WAPE and 20% MAPE, helping improve demand planning and inventory decisions.
IBM Developer Badge for Python for Data Science & AI
IBM
June 1, 2026 – Present
NPTEL course certificate in Introduction to Machine Learning
NPTEL
June 1, 2026 – Present
Great learning course certificate in Data visualization with Power BI
Great Learning
June 1, 2026 – Present
Python Programming certified by Live Wire Training Institute
Live Wire Training Institute
June 1, 2026 – Present
Data visualization with Tableau by Cognitive I IT Solutions
Cognitive I IT Solutions
June 1, 2026 – Present
Great learning course certificate in Deep Learning
Great Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a diverse application of AI/ML skills across different domains (finance, e-commerce, sports analytics), indicating adaptability and a broad interest in problem-solving. The target role of 'AI Engineer' aligns well with the candidate's project experience in building AI-driven applications and systems, including LLM-based solutions. The breadth of skills listed, from traditional ML to Generative AI, suggests a willingness to learn and apply various technologies, which is a positive for cultural fit in an innovative environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems and deliver solutions, suggesting good problem-solving skills. The mention of collaborative environments in the summary implies team collaboration potential. However, without direct assessment data, specific soft skills like communication clarity, stress handling, or detailed work attitude cannot be fully evaluated.