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AI Engineer with 2+ years in Trust & Safety Risk Investigation, specializing in Machine Learning and
Data-driven professional with 2+ years of experience at PhonePe in Trust & Safety-Risk Investigation, specialising in high-volume transaction analysis, fraud detection, and SQL-based data review. Completed 1 year in IBM's Artificial Intelligence and Machine Learning program, with strong skills in Python, SQL, Machine Learning, Generative AI, and data analytics. Developed a Hybrid AI Decision Intelligence System for real-time fraud monitoring and risk assessment, as well as a Medical Knowledge Assistant leveraging LLMS, LangChain, Pinecone, and OpenAI APIs. Passionate about applying AI and machine learning to build intelligent solutions for fraud prevention, risk intelligence, and business decision-making.
NEW HORIZON COLLEGE OF ENGINEERING
BTech · Electrical And Electronics Engineering
N/A – June 30, 2026
SHARANABASAVESHWARA RESIDENTIAL PU COLLEGE
Pre University Education
N/A – Present
SHARANABASAVESHWARA RESIDENTIAL PUBLIC SCHOOL
Secondary Education Examination Board
N/A – Present
PHONEPE
Trust & Safety – Risk Investigation
July 1, 2024 – May 1, 2026
India
PHONEPE
CX Operations Specialist (Intern – MX Lending)
February 1, 2024 – June 1, 2024
India
Hybrid AI Decision Intelligence System for Transaction Risk
June 25, 2026 – Present
• Designed and developed a hybrid fraud risk detection system using Python, combining deterministic rule-based logic with a RandomForestClassifier (scikit-learn) for probabilistic risk modeling. • Performed feature engineering on transactional and behavioral indicators (transaction amount, account age, velocity patterns, device/location changes) using NumPy and Pandas. • Trained and deployed a scikit-learn Random Forest model to predict fraud probability and generate dynamic risk scores. • Integrated LLM-powered explainable AI using the OpenAI API to generate human-readable transaction risk justifications. • Structured a modular project architecture separating UI layer (Streamlit), ML pipeline (scikit-learn), and AI explanation module (OpenAI API). • Implemented a hybrid scoring mechanism integrating rule-based evaluation (60%) and ML-based probability outputs (40%) to simulate production-style risk decision systems.
Medical Knowledge Assistant using LLMs and Vector Search
June 25, 2026 – Present
• Developed AI-powered medical chatbot using LangChain, OpenAI API, and Pinecone for semantic document retrieval. • Integrated LLM with document embedding system to deliver contextually accurate and relevance-based medical information retrieval. • Built Flask web application with real-time query processing and vector embedding capabilities. • Deployed on AWS with Docker containerisation and automated GitHub Actions CI/CD pipeline. • Integrated LLM with RAG system for contextually accurate medical information retrieval.
Completed Power BI Certification
Data Flair
June 1, 2026 – Present
Artificial Intelligence /Machine Learning Certificate
IBM
February 1, 2025 – February 1, 2026
Cultural Fit Analysis
The candidate's projects demonstrate a proactive and self-driven approach to learning and applying advanced AI/ML concepts, which aligns well with an innovative and growth-oriented culture. The diversity of projects (fraud detection, medical chatbot) shows a broad interest in applying AI to different domains. Their experience in a fast-paced fintech environment (PhonePe) suggests adaptability and resilience. The focus on practical, real-world problem-solving (e.g., hybrid scoring mechanism, explainable AI) indicates a results-oriented mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to structure modular architectures and integrate various technologies, suggesting good problem-solving and system thinking skills. Their experience in risk investigation at PhonePe implies attention to detail and an analytical mindset. The automation of risk ticket prioritization using SQL also points to an efficiency-driven approach. Collaboration with internal teams for risk escalation shows teamwork and communication skills.