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AI Engineer with 4+ years in Machine Learning, Generative AI & Cloud Systems.
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Highly skilled AI Engineer with 4.9 years of experience, specializing in Machine Learning, Generative AI, and Distributed Systems. Proven track record in developing and deploying end-to-end ML pipelines, RAG systems, and agentic workflows. Expertise in data-centric anomaly detection, battery state estimation, and hardware-aware deep learning design, with a strong foundation in Python, AWS, and MLOps practices.
University of Warwick
Visiting Research Student · Scientific Computing & Optimisation
August 1, 2020 – June 30, 2020
Indian Institute of Technology (IIT), Kharagpur
Integrated M.Sc. · Mathematics and Computing
August 1, 2016 – June 30, 2021
JPMorgan Chase
Machine Learning Engineer (Applied GenAI)
June 1, 2025 – Present
Mumbai, Maharashtra, India
Samsung Semiconductor India Research
Software Developer 2 (Applied ML Research)
June 1, 2022 – June 1, 2025
Bengaluru, Karnataka, India
Ceremorphic
Deep Learning Engineer (Systems & Compute)
June 1, 2021 – May 1, 2022
Hyderābād, Telangana, India
Meta × HuggingFace × PyTorch Hackathon
January 1, 2024 – May 26, 2024
Selected among 800 teams from 31,000+ participants at India's largest GenAI hackathon. Built a Medallion Architecture RL agent fine-tuning Qwen2.5-1.5B via SFT and then optimized with GRPO using Unsloth against a composite DQS reward. Designed a hybrid reward function combining DQS delta (completeness, uniqueness, validity, freshness), step cost penalties, and terminal crash/completion bonuses; enforced row floor and quarantine ceiling guards to prevent reward hacking.
View ProjectOffline Face Clustering for Privacy-Preserving Organization
January 1, 2024 – May 26, 2024
Built an offline, privacy-preserving pipeline to automatically sort 2,326 Sony RAW (.ARW) files by identity - replicating Google Photos face grouping with zero cloud uploads. Extracted face embeddings using InsightFace (ArcFace); applied HDBSCAN clustering to discover 64 identities automatically from ~3,600 detected faces with zero incorrect identity assignments.
View ProjectCultural Fit Analysis
The candidate exhibits a strong cultural fit through diverse experiences and a proactive, learning-oriented mindset. Their involvement in a GenAI hackathon showcases a collaborative spirit and drive for innovation. The breadth of their projects, from privacy-preserving face clustering to hardware-aware deep learning, indicates intellectual curiosity and adaptability. Their academic background from IIT Kharagpur and visiting research, coupled with publications, underscores a commitment to continuous learning and a research-driven approach, which is highly valuable in an evolving AI landscape.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project designs and optimizations. Their participation in a major hackathon highlights innovation, collaboration, and the ability to perform under pressure. They exhibit strong system thinking by architecting end-to-end pipelines with robust logging, alerting, and human-in-the-loop validation, indicating a meticulous approach to operational excellence and data integrity.