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AI Engineer with less than a year in ML systems, LLM-powered applications, and RAG pipelines.
AI & Data Science graduate with hands-on experience building end-to-end ML systems, LLM-powered applications, and RAG pipelines. Skilled in Python, FastAPI, and cloud deployment (AWS). Passionate about applying Generative AI and AI Agents to solve real-world problems in security automation and intelligent systems.
Gnanamani College of technology
B.Tech · Artificial Intelligence & Data Science
August 1, 2021 – June 30, 2025
Qubinex technologies Pvt Ltd
Data Engineer Intern
December 1, 2025 – March 1, 2026
Chennai, Tamil Nadu, India
Pantech Prolabs India Pvt Ltd
Artificial Intelligent Intern
June 1, 2023 – July 1, 2023
Coimbatore, Tamil Nadu, India
LLM-powered threat sentiment classifier
June 23, 2026 – Present
Fine-tuned BERT 50k+ social media posts to extract contextual embeddings from social media posts. Combined BERT embeddings with XGBoost and engineered features for downstream classification. Trained and optimized XGBoost, including hyperparameter tuning and cross-validation. Improved model performance by ~18% in overall F1 score versus the baseline model. Deployed using Flask/FastAPI and Hosted on AWS/Hugging Face.
View ProjectData Analytics Professional Certificate
IBM
June 1, 2026 – Present
DATA SCIENTIST
ACTE Technologies
September 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects and internships demonstrate a focus on AI and data science, which aligns well with an AI Engineer role. The personal project on threat sentiment classification shows initiative and an interest in applying AI to real-world problems. The diversity of tools and platforms listed (AWS, Docker, Git, SQL, Tableau, Power BI) suggests a willingness to learn and adapt to different technical environments. However, the experience is primarily academic and internship-based, so the depth of experience in collaborative, production-level environments is not fully evident.
Soft Skills & Operational Fit
The candidate lists problem-solving, creative thinking, fast learner, communication, and teamwork as soft skills. While these are valuable, there is no assessment data to validate these claims. The project and internship descriptions suggest an ability to work on defined tasks and contribute to technical solutions. The candidate's academic background in AI & Data Science indicates a foundational fit for an AI-centric role.