Yuval Mehta
Generative AI Engineer · Mumbai, India
Building agentic AI systems, scalable ML pipelines, and production-ready LLM deployments.
Generative AI Engineer @ xLM
Continuous Intelligence
Top 1%
Amazon ML Challenge 2024
2×
IEEE Published Researcher
2+
Years Building AI Systems
15+
Technical Articles on Medium
genai.works Hackathon 2025
Top 4 finish with the xLM team — competing against industry AI teams, July 2025
Amazon ML Challenge 2024
Top 1% globally — outranked 74,820+ participants across 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 — Early Diabetes Prediction using ML approaches, ensemble methods & feature engineering
IIT Kharagpur Research
Invited ML Research Intern — contributed GNN + autoencoder research at one of India's premier IITs
Production AI Impact
Shipped LangGraph agents in GxP-regulated environments — 65% task automation, 100% traceability
// about
I'm a Generative AI Engineer from Mumbai building production-grade agentic systems and LLM pipelines. I don't just prototype — I ship AI that works under real constraints: compliance-heavy environments, high-stakes data, and zero tolerance for hallucination.
Currently at xLM – Continuous Intelligence, I architect LangGraph-based multi-agent systems for GxP compliance automation in the life sciences industry. I've also done ML research at IIT Kharagpur, built deep learning pipelines for KYC at JM Financial, and competed at the top 1% level in India's largest ML challenge.
My sweet spot is the intersection of research and engineering — I care about model accuracy AND system reliability. Whether it's RAG pipelines, fine-tuned LLMs, computer vision models, or MLOps infrastructure, I build things that are auditable, scalable, and production-ready.
I also write on Medium about LLMOps, context engineering, and agentic AI patterns — because I believe good engineers share what they learn. If you're building something ambitious in AI, 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
- • Engineered AI agents within cIV, automating GxP compliance workflows and expanding task coverage by 65%
- • Orchestrated LangGraph-based multi-agent systems with retry, memory, and control flows — slashing execution time by 30% and raising success rate by 40%
- • Embedded traceable logic across workflows, enhancing audit-readiness in collaboration with engineering and QA
AI/ML Intern
xLM Continuous Intelligence·Mumbai, Maharashtra
Jan – May 2025
- • Traceability matrix generator: -60% manual overhead, +45% consistency score
- • Prototyped 3 AI-driven document intelligence solutions, accelerating internal validation cycles by 50%
- • Deployed real-time QA pipelines achieving 100% traceability in early-stage toolchains
Machine Learning Intern
IIT Kharagpur·Remote
Jul 2024 – May 2025
- • Devised a video feature extractor using autoencoders and GNNs, improving frame processing efficiency by 30%
- • Optimized hyperparameters to cut training duration by 25% and raise validation accuracy by 18%
Data Science Intern
JM Financial Ltd·Mumbai, Maharashtra
Jul – Nov 2024
- • Streamlined KYC document verification with OCR and deep learning, reducing processing time by 40%
- • Synthesized dashboards and data pipelines, enhancing insight delivery throughput by 3×
Backend Developer Intern
Kenmark ITAN Solutions·Mumbai
Dec 2022 – Apr 2023
- • API engineering improvements: +30% integration efficiency across platforms
- • QA protocols implementation: +20% system reliability
- • SQL query optimization: -15% average query execution time
// 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
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
Building ML in the Dark: A Survival Guide for the Solo Practitioner
→Apr 2026
How Multi-Agent Self-Verification Actually Works (And Why It Changes Everything for Production AI)
→Mar 2026
Your AI Agent’s Memory Is Broken — And Here’s How to Fix It
→Mar 2026
How to Evaluate LLMs When There Is No Ground Truth
→Feb 2026
Prompt Versioning Is the New Model Versioning
→Jan 2026
LLMOps Is Not MLOps: Why Your LLM Demo Broke in Production (With Real Examples)
→Dec 2025
Context Engineering: The Hidden Power Behind Smarter AI Systems
→Dec 2025
// 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.