AI Engineer with less than a year in Deep Learning & NLP
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Assessing your cultural and operational fit
Early-career AI Engineer and Master of Data Science graduate with a strong foundation in deep learning, applied machine learning, and hands-on experience developing, training and integrating ML models into end-to-end workflows using Python and modern frameworks. Experienced across the end-to-end ML lifecycle, including data preprocessing, model experimentation, evaluation, and optimisation. Background includes working with transformer-based models, large datasets, with a solid understanding of core ML concepts, algorithms, and performance trade-offs. A collaborative problem solver motivated by building accurate, efficient, and scalable ML systems in real-world environments.
RMIT University
Master of Data Science · Data Science
February 1, 2023 – December 1, 2024
SJB Institute of Technology
Bachelor of Engineering · Computer Science
August 1, 2018 – July 1, 2022
Data Prowess Pvt Ltd
Applied AI Engineer
November 1, 2025 – Present
India
Makers Lab - Tech Mahindra
Data Science Intern
July 1, 2024 – November 1, 2024
Melbourne, Victoria, Australia
Customer Churn Prediction - End-to-End Machine Learning Project
June 2, 2026 – Present
Built an end-to-end churn prediction system using structured customer data of 7,000 observations, covering Exploratory Data Analysis (EDA), feature engineering, modelling, and evaluation with business-aligned metrics. Trained and compared Logistic Regression and Random Forest models; selected Logistic Regression for interpretability and optimized the decision threshold to achieve ~86% recall for churn prevention. Applied model explainability using Logistic Regression coefficients and SHAP (global and local) to identify key churn drivers such as tenure, pricing, contract length, payment behaviour, and support services.
Inventory Management System for Leela Glass, Plywood and Hardware (Freelance Project)
June 2, 2026 – Present
Designed and developed a custom inventory management system for a hardware retailer, enabling accurate tracking of cost price and item quantities across hundreds of SKUs, replacing manual stock tracking workflows. Implemented RESTful API endpoints to manage inventory operations (stock in/out), supplier data, and searchable, category-based product records, enabling clean separation between backend logic and UI. Built role-based authentication and reporting features (including export-ready summaries), helping the business reduce stock errors, save operational time, and maintain real-time inventory accuracy.
Cultural Fit Analysis
The candidate's academic and freelance projects, combined with their internship and current role, demonstrate a diverse range of experiences from traditional ML to advanced LLM applications. This breadth of exposure and continuous learning aligns well with a dynamic AI engineering environment. The focus on end-to-end solutions and practical deployment indicates a results-oriented mindset.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a problem-solving approach, collaboration with researchers, and a motivation for building accurate, efficient, and scalable ML systems. The freelance project also indicates initiative and client-facing skills. The detailed descriptions of complex systems suggest strong analytical and organizational skills.