I design and build production-grade generative AI systems with strong observability and robust system architectures.
From multi-agent LLM orchestration systems to enterprise RAG platforms and cloud-native MLOps pipelines, I design and deploy production generative AI systems for real-world applications.
How I approach AI architecture, from design to deployment.
Agentic Systems
Multi agent orchestration, tool use, structured outputs, and guardrails.
Grounded GenAI
RAG pipelines, evidence grounding, evaluation, and hallucination mitigation.
Cloud & MLOps
Deployments on Azure/AWS with CI/CD, monitoring, and compliance first design.
About
I am an Applied AI Engineer at Deloitte, where I work on cloud-based multi-agent large language model (LLM) systems for federal healthcare and enterprise clients. My work focuses on designing and deploying generative AI systems that operate in real production environments.
In practice, this involves designing agentic workflows using LangGraph and LangChain, integrating retrieval-augmented generation (RAG), and developing end-to-end AI systems from model development to production deployment across cloud platforms.
Alongside industry work, I have a strong academic foundation in deep learning and applied machine learning. I have published peer-reviewed research with Springer on medical image classification using fine-tuned deep learning models and have hands-on experience deploying machine learning systems beyond research prototypes.
I am also the founder of the Data Science Club at Charusat University, where I led initiatives in applied machine learning, collaborative learning, and hands-on technical projects. These experiences shaped how I approach AI today: as an engineering discipline grounded in theory, systems thinking, and real-world constraints.
This portfolio highlights selected projects, architectures, and research that showcase how I design, implement, and reason about production-grade AI systems.
Founder, Data Science Club — Charusat University · Club website
Experience
Building production-grade AI systems at scale.
Generative AI Developer
Deloitte Consulting · Pennsylvania, United States
Leading the design and deployment of production-grade generative AI systems for federal healthcare clients, focusing on reliability, scalability, and real-world impact.
- Designed and deployed multi-agent LLM orchestration systems for NIH, CDC, and VBA.
- Delivered AI-driven product development supporting new CDC contracts.
- Built monitoring, logging, and alerting for production AI services.
- Reduced service latency by 76% through async workflows and token optimization.
- Owned CI/CD pipelines and operational readiness.
- Implemented LLMOps / MLOps pipelines with evaluation and retraining hooks.
- Enhanced distributed LLM workflows using MCP.
Data Analyst
Eminence Technology Solutions · New Jersey, United States
Focused on building and optimizing large-scale data pipelines supporting AI and ML workflows in healthcare.
- Built pipelines processing ~6 TB of healthcare data.
- Automated ETL workflows across Spark clusters.
- Streamlined data pipelines for faster model development.
Machine Learning Intern
Fractal Analytics · New York, United States
Worked on deploying and optimizing production ML models with monitoring and evaluation.
- Deployed production ML models with monitoring.
- Built a graph-based recommendation system.
Education
Academic foundation supporting applied AI engineering and large-scale data systems.
Bachelor of Technology in Information Technology
Charusat University · Gujarat, India
Undergraduate program focused on computer science fundamentals, including software engineering, algorithms, and applied information technology.
Projects
Grounded Research Assistant
Autonomous Debugger Assistant
A deterministic, multi-agent AI system that autonomously analyzes software failures, identifies root causes, generates safe code fixes, and validates them using a LangGraph-controlled execution loop.
- Evaluator-driven bounded retry loop ensuring convergence
- Structured system for coordinating multi-agent workflow
- Safety guardrails for controlled autonomous code changes
BoneX — Multi Bone Fracture Detection System
TravelBuddy — Multi Agent AI Travel Assistant
Publications
Classification of Choroidal Neovascularization (CNV) from Optical Coherence Tomography (OCT) Images Using Efficient Fine-Tuned ResNet and DenseNet Deep Learning Models
Age-related macular degeneration (AMD) is a macular degenerative disease and a leading cause of blindness worldwide, often associated with choroidal neovascularization (CNV). This study focuses on the classification of CNV from Optical Coherence Tomography (OCT) images using efficient fine-tuned ResNet and DenseNet deep learning models. The proposed approach aims to accurately identify CNV to support early diagnosis and treatment. Model performance is evaluated and compared to determine the most effective architecture for CNV classification, contributing toward more reliable and efficient AI-assisted diagnostic tools in ophthalmology.
Venue: Springer — Intelligent Computing / Artificial Intelligence (Book Chapter)
Agent-Based Research Workflows for Evidence-Grounded Analysis
This technical report explores agent-based orchestration for automated research analysis, emphasizing evidence grounding, structured outputs, and iterative refinement. The workflow is designed to support reproducibility, critical review, and human-in-the-loop validation in research and applied AI systems.
Status: Technical report / preprint
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