ML Engineer with less than a year in AI/ML model deployment
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ML Engineer with hands-on research experience building and deploying predictive models on a government-funded smart city project (RUSA 2.0) at CUSAT. UGC NET qualified professional seeking an AI/ML engineering role to ship production-ready models. Experienced in delivering full ML pipelines from data cleaning to deployment across various algorithms including XGBoost and CatBoost.
University of Calicut
MSc · Computer Science
N/A – June 30, 2025
College of Applied Science, Vazhakkad
BSc · Computer Science
N/A – June 30, 2023
Cochin University of Science and Technology (CUSAT)
Junior Research Fellow
October 1, 2025 – Present
Cochin, Kerala, India
Smart Policing CNN Crime Classification Engine
June 23, 2026 – Present
Trained a CNN text classifier on crime reports to automate categorization, replacing manual triage processes. Developed a rule-augmented risk scoring module combining model confidence with victim vulnerability features and keyword severity. Deployed the model via a Flask REST API, integrating it with both a web portal and an Android application.
Smart Home - Property & Design Marketplace
June 23, 2026 – Present
Built a multi-stakeholder web platform handling four distinct user roles (homeowners, architects, contractors, vendors) and permission sets. Users can browse pre-defined designs, request custom plans and compare architect budgets.
Full Stack Development (MERN)
KASE, Edunet Foundation, and EY
June 1, 2026 – Present
UGC NET Computer Science
NTA
June 1, 2024 – Present
Cultural Fit Analysis
The candidate's experience in a research fellow role at a university and involvement in a government-funded project suggests a preference for structured environments and projects with clear objectives. Their personal projects demonstrate initiative and a breadth of interest beyond core ML, including full-stack development. The 'Smart Policing' project shows an interest in applying ML to real-world societal problems, which aligns with impact-driven organizations. The 'Smart Home' project indicates an ability to handle complex multi-user systems. The candidate's academic background and certifications further support a continuous learning mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their research fellow role and project work, particularly in optimizing models and identifying key drivers. Their focus on reproducible experiments suggests an organized and detail-oriented approach. The ability to work on government-funded projects implies adherence to structured processes and documentation. Collaboration is implied through project work involving multiple stakeholders and integration with different applications.