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AI Engineer with 4+ years in Fraud Detection & GenAI Solutions
AI & Machine Learning Engineer with 4+ years of experience building and deploying production-grade ML systems in financial services and regulated environments. Skilled in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) and Generative AI (GenAI), including RAG and LLMs. At Razorpay, developed real-time fraud detection and credit risk models using Apache Kafka, PySpark, XGBoost and LightGBM, contributing to 76% YoY revenue growth. Currently at PwC, delivering GenAI solutions using Azure OpenAI, LangChain and Azure AI Search, and building LLM evaluation frameworks and AI governance systems for UK financial institutions. Experienced in MLOps / LLMOps, including model deployment, monitoring and CI/CD, with Python, PyTorch, MLflow, Docker, Microsoft Azure and AWS.
University of Hertfordshire
Master of Science · Artificial Intelligence & Robotics
N/A – June 30, 2023
PwC
AI & ML Engineer
November 1, 2024 – Present
London, England, United Kingdom
Razorpay
Machine Learning Engineer
September 1, 2019 – August 31, 2022
Mumbai, Maharashtra, India
Cultural Fit Analysis
The candidate's experience spans both established financial institutions (PwC, Razorpay) and cutting-edge AI/ML development, indicating adaptability to different organizational cultures. Their work on AI governance, responsible AI, and adversarial testing suggests a strong ethical compass and a proactive approach to potential risks, which aligns well with a culture valuing responsibility and foresight. The diversity of projects, from fraud detection to GenAI applications, shows a broad interest and ability to contribute across various domains, fostering a collaborative and innovative environment.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong problem-solving skills through complex project descriptions and quantifiable achievements. Their experience in regulated environments (financial services) suggests an understanding of compliance and robust system design. The detailed descriptions of evaluation frameworks and adversarial testing indicate a methodical and quality-focused approach. The ability to work with diverse technologies (Azure, AWS, Kafka, Spark) points to adaptability and a broad operational fit.