Yuval Mehta
Generative AI Engineer · Mumbai, India
Building regulated, production-grade AI platforms - from agentic workflows and LLM infrastructure to scalable ML systems.
Generative AI Engineer @ xLM
Continuous Intelligence
Top 1%
Amazon ML Challenge 2024
5x
Infrastructure cost reduction
65%
Workflow coverage expanded
2
IEEE publications
15+
Technical Articles on Medium
GenAI Week 2025 Hackathon
Placed 4th of 250+ teams in Silicon Valley after building a GxP audit chatbot end to end.
Amazon ML Challenge 2024
Ranked 274th of 74,824 participants - Top 1% in India's largest ML competition.
IEEE InCoWoCo 2025
Published: Estimating Ground-Level AQI from Satellite Imagery using dual-view attention models.
IEEE APCIT 2024
Published: Examining ML Approaches for Early Diabetes Prediction.
IIT Kharagpur Research
Built a spatiotemporal video-feature extraction pipeline using autoencoders and graph neural networks.
Production AI Platform Delivery
Owned cEMS architecture and delivery, including ML pipelines, agents, CI/CD, and GxP-compliant infrastructure.
// about
I'm a Generative AI Engineer from Mumbai who builds dependable AI systems for real operating environments - not just demos. My work spans agentic workflows, LLM infrastructure, distributed systems, and ML platforms where compliance, observability, and reliability matter from day one.
At xLM - Continuous Intelligence, I own architecture and delivery for GxP-compliant products: from cEMS, an environmental-monitoring platform built from the ground up, to reusable audit-trail infrastructure and LangGraph multi-agent systems for intelligent validation. That work has reduced infrastructure costs 5x, improved execution time 30%, increased task success 40%, and expanded workflow coverage 65%.
I combine research depth with product-minded engineering. My background includes machine-learning research at IIT Kharagpur, computer-vision and OCR work at JM Financial, two IEEE publications, and a Top 1% finish in the Amazon ML Challenge. I care equally about model capability, system design, and measurable business impact.
I also share practical lessons on LLMOps, context engineering, and agentic AI patterns. If you're building high-trust AI infrastructure or an ambitious AI product, let's talk.
// skills
ML/DL
Generative AI & LLMs
MLOps & Cloud
Languages
Databases & Vector Stores
Web & APIs
Data & Big Data
// experience
Generative AI Engineer
xLM Continuous Intelligence·Mumbai, Maharashtra
Jun 2025 - Present
- • Owned architecture and delivery of cEMS from the ground up, building distributed systems, ML pipelines, AI agents, CI/CD workflows, and GxP-compliant infrastructure
- • Reduced cTM infrastructure costs 5x through end-to-end pipeline redesign, scalability improvements, and performance optimization
- • Built a reusable production audit-trail system that captures actor actions and contextual metadata for GxP-compliant traceability across products
- • Designed LangGraph multi-agent systems and key cIV components, improving execution time 30%, task success 40%, and workflow coverage 65%
AI/ML Intern
xLM Continuous Intelligence·Mumbai, Maharashtra
Jan - May 2025
- • Built a traceability matrix generator that reduced manual overhead 60% and improved workflow consistency 45%
- • Prototyped three AI-driven document-intelligence solutions, reducing internal validation-cycle time 50%
Machine Learning Intern
IIT Kharagpur·Kharagpur, West Bengal
Jul 2024 - May 2025
- • Built a video feature-extraction pipeline using autoencoders and graph neural networks for spatiotemporal representation learning, improving frame-processing efficiency 30%
Data Science Intern
JM Financial Ltd·Mumbai, Maharashtra
Jul - Nov 2024
- • Automated KYC document verification using computer vision and deep learning, reducing processing time 40%
- • Developed OCR solutions that increased document-verification efficiency 30%
- • Analysed large datasets to generate actionable insights that improved operational efficiency 15%
Backend Developer Intern
Kenmark ITAN Solutions·Mumbai, Maharashtra
Dec 2022 - Apr 2023
- • Engineered APIs that increased cross-platform integration efficiency 30%
- • Implemented QA protocols that improved system reliability 20%
- • Optimised SQL and MySQL queries, reducing average execution time 15%
// projects
ImageLingo is an image captioning project that uses deep learning to generate captions for images. The project is built using PyTorch and includes training, evaluation, and deployment components.
This project focuses on classifying urban sounds using deep learning techniques. The goal is to accurately identify different types of sounds commonly found in urban environments.
VerbalVision is a deep learning-based lip reading application inspired by the LipNet model. It processes video frames to extract lip regions and predicts the spoken words.
This project is a Streamlit application designed to help users generate cold emails, skill gap analyses, and cover letters based on their resume and job postings.
This project implements an AI-powered job scheduling system that combines Reinforcement Learning (RL) and traditional scheduling algorithms to optimize job scheduling. The system is designed to predict job schedules, evaluate performance metrics, and compare RL-based scheduling with baseline algorithms.
A machine learning application that predicts Air Quality Index (AQI) and air pollutant concentrations using street view and satellite images.
// writing
From Lumiere to Veo 3: How Google Solved the Hardest Problem in Video Generation
→Aug 2026
Prefill-Decode Disaggregation: When and Why to Split Your Inference Stack
→Jul 2026
The Hidden Cost of Agent Memory: What Mem0, Zep, and Letta Don’t Tell You
→Jul 2026
Reward Design Is the Hard Part: Building Verifiable Rewards for Tool-Using Agents
→Jul 2026
LangGraph Checkpointing Is Not Free: A Production Postmortem
→Jun 2026
GRPO in Production: The Failure Modes Nobody Writes About
→Jun 2026
Mechanistic Interpretability Is Having Its Moment: What Engineers Actually Need to Know
→Jun 2026
Reasoning Models Don’t Reason the Way You Think
→May 2026
World Models Explained: The Architecture That Could Replace Transformers
→Apr 2026
The Death of RLHF: A Practitioner’s Guide to the New Post-Training Stack
→Apr 2026
// research
Estimating Ground-Level Air Quality Index from Satellite Imagery
Yuval Mehta et al.
A dual-view attention model combining satellite and street-view imagery to forecast AQI and six pollutants, achieving 93% R² accuracy with 35% reduction in cloud training costs.
Examining ML Approaches for Early Diabetes Prediction
Yuval Mehta et al.
Explores multiple ML models for early diabetes prediction, highlighting key patterns in patient health data to aid proactive healthcare measures. Demonstrates the effectiveness of ensemble methods and feature engineering in medical diagnostics.
// contact
Let's build something.
Open to full-time roles, research collaborations, and freelance AI/ML projects.