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AI Engineer with 1+ years in Generative AI, Machine Learning & Data Systems
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M.Tech graduate with 1.6+ years of experience in AI/ML engineering, scalable data systems, and Generative AI applications. Skilled in developing machine learning pipelines, predictive models, RAG-based systems, and data processing workflows using Python, SQL, and cloud technologies. Hands-on experience in model validation, feature engineering, explainable AI, and production-ready ML solutions across healthcare and financial domains. Published 4 research papers with 1700+ reads in AI-driven healthcare and financial risk systems.
Vellore Institute of Technology
M.Tech · Computer Science and Engineering
January 1, 2023 – January 1, 2025
Savitribai Phule Pune University
B.E · Information Technology
January 1, 2019 – January 1, 2023
Bitspark Technologies
AI/ML Engineer
August 1, 2025 – February 1, 2026
Pune, Maharashtra, India
Dumroo.ai
AI/ML Intern
July 1, 2025 – August 1, 2025
India
Vellore Institute of Technology
Research Scholar
July 1, 2024 – May 1, 2025
Vellore, Tamil Nadu, India
Agentic AI Framework for Continuous Credit Risk Model Validation
January 1, 2026 – Present
Designed and engineered a production-grade Agentic AI framework using LangGraph and Gemini that autonomously performs continuous credit risk model validation and governance, significantly reducing manual effort in highly regulated financial environments. Solved the complex challenge of maintaining regulatory compliance and model reliability in dynamic conditions by orchestrating a stateful multi-agent system (5 specialized agents) for real-time drift detection, bias auditing, explainability, and IFRS 9 compliance validation. Built an end-to-end ML pipeline on 2.2M+ records using XGBoost and Logistic Regression to develop robust Probability of Default (PD) models, while integrating agentic workflows for continuous performance monitoring. Architected a hybrid GraphRAG + Vector RAG system (Neo4j + FAISS) enabling intelligent, context-aware querying and synthesis of complex regulatory documents (IFRS 9, RBI, ECB), allowing agents to reason over evolving compliance requirements. Implemented statistical validation layers using SciPy to compute PSI, KS Statistics, SHAP-based explainability, enabling proactive identification and mitigation of model risk and data drift. Developed a complete IFRS 9 Expected Credit Loss (ECL) engine with Stage 1/2/3 classification, SICR logic, and macroeconomic scenario analysis; deployed as scalable FastAPI services with Streamlit monitoring dashboards for real-time governance.
View ProjectGenerative AI with Large Language Models
Coursera
June 1, 2026 – Present
Machine Learning in Production
Coursera
June 1, 2026 – Present
AWS Certified AI Practitioner
LinkedIn Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, spanning financial risk management and healthcare, indicates adaptability and a broad interest in applying AI across different sectors. Their involvement in research and mentoring suggests a proactive and collaborative approach, which aligns well with an innovative and learning-oriented culture. The focus on explainable and auditable AI outputs in their research and projects also points to a strong sense of responsibility and ethical considerations in AI development. The candidate's experience with both academic research and industry roles suggests a balanced perspective on theoretical and practical applications.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations (e.g., Agentic AI framework for credit risk). Their experience in orchestrating multi-agent systems and integrating various technologies suggests good organizational and system-thinking abilities. The research scholar role and mentoring experience indicate a collaborative mindset and a willingness to share knowledge. The project descriptions are detailed, implying good written communication, though no direct communication assessment was provided.