AI Engineer with less than a year in LLM & Generative AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
B.Tech undergraduate specializing in Artificial Intelligence & Machine Learning with hands-on experience building machine learning systems, LLM-powered applications, and scalable data-driven solutions across internships in ML, web development, and IT infrastructure. Experienced in developing supervised learning pipelines using XGBoost, Random Forest, BERT, and TensorFlow, alongside architecting Retrieval-Augmented Generation (RAG) systems with LLaMA 3.1, LangChain, and real-time web retrieval frameworks. SAP Certified Generative AI Developer with strong foundations in NLP, predictive modeling, backend APIs, and applied AI research, seeking high-impact AI/ML internship opportunities in machine learning engineering, GenAI, and intelligent product development.
Hindustan Institute of Technology and Science
B.Tech · CSE – Artificial Intelligence & Machine Learning
August 1, 2023 – June 30, 2027
Sri Ayyan Vidyashram Higher Secondary School
Higher Secondary Education (Class XII)
June 1, 2011 – May 31, 2023
Sri Ayyan Vidyashram Higher Secondary School
Secondary Education (Class X)
June 1, 2011 – May 31, 2023
ScalePods
AI Automation Engineer Intern
June 1, 2026 – Present
India
TransNeuron
AI Developer Intern
May 1, 2026 – June 1, 2026
India
Prodigy InfoTech
Machine Learning Intern
July 1, 2025 – August 1, 2025
Mumbai, Maharashtra, India
Fake News Verification System — LLaMA 3.1 + RAG
June 26, 2026 – Present
Architected an LLM-powered misinformation detection system combining LLaMA 3.1 8B inference with Tavily Search API for real-time retrieval-augmented verification of news claims. Designed a prompt engineering pipeline that structured LLaMA 3.1 reasoning over retrieved external evidence — enabling contextual, source-grounded verdicts rather than static pattern matching. Integrated Tavily for live web retrieval so the system cross-references claims against current sources, enabling temporal generalization beyond training data. Evaluated system outputs against ground-truth labelled datasets using precision, recall, and F1-score; documented failure modes (out-of-scope claims, ambiguous context) for further iteration.
View ProjectAI-Powered Personal Healthcare Recommendation System
June 26, 2026 – Present
Built an ML-driven health recommendation system that ingests user lifestyle inputs (dietary habits, activity levels, biometric indicators) and generates personalized guidance using trained classification and regression models. Implemented an end-to-end data pipeline for cleaning, feature extraction, and model inference; designed an API layer to serve recommendations based on user input parameters.
View ProjectSAP Certified – SAP Generative AI Developer
SAP
April 1, 2026 – May 1, 2027
SAP Certified Associate – Data Analyst, SAP Analytics Cloud
SAP
January 1, 2026 – January 1, 2027
Retrieval-Augmented Generation for Enhanced AI Outputs
IBM
January 1, 2026 – Present
Google Cloud Associate
Google Cloud
January 1, 2026 – Present
Supervised Machine Learning: Regression & Classification
DeepLearning.AI / Coursera
January 1, 2025 – Present
The candidate achieved a perfect score (100%) on the 'Data Scientist — Artificial Intelligence' test, indicating exceptional proficiency in the evaluated skills.
Strengths
Limitations
Cultural Fit Analysis
The candidate's diverse project portfolio, including a misinformation detection system and a healthcare recommendation system, demonstrates a breadth of interest and application areas within AI. Their internships at ScalePods, TransNeuron, and Prodigy InfoTech show adaptability to different organizational contexts (AI automation, AI development, ML engineering). The certifications, especially in SAP Generative AI and Google Cloud, indicate a proactive learning attitude and a desire to stay current with industry trends, which aligns well with a dynamic AI engineering environment. The candidate's focus on practical, real-world problem-solving through AI suggests a strong cultural fit for an innovative and impact-driven team.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experiences suggest a collaborative approach, iterative development, and a focus on optimizing workflow reliability and output quality. The psychometric test score (273/500) indicates potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration, which would require further investigation during interviews. However, the detailed project descriptions show an ability to articulate complex technical work clearly.