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I’m Suyash Naresh Verma, a data science and AI engineering professional pursuing an MS at Arizona State University. I enjoy building scalable ML systems, LLM-powered applications, and data processing pipelines, with hands-on experience across academia and industry. I thrive in collaborative environments, turning complex data into actionable insights, and delivering end-to-end ML solutions—from model development to deployment and observability. I’m passionate about clean code, robust architectures, and continuous learning.…I’m Suyash Naresh Verma, a data science and AI engineering professional pursuing an MS at Arizona State University. I enjoy building scalable ML systems, LLM-powered applications, and data processing pipelines, with hands-on experience across academia and industry. I thrive in collaborative environments, turning complex data into actionable insights, and delivering end-to-end ML solutions—from model development to deployment and observability. I’m passionate about clean code, robust architectures, and continuous learning.

Suyash Naresh Verma

AI Engineer, AI Developer, Full Stack Developer, +3

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I’m Suyash Naresh Verma, a data science and AI engineering professional pursuing an MS at Arizona State University. I enjoy building scalable ML systems, LLM-powered applications, and data processing pipelines, with hands-on experience across academia and industry. I thrive in collaborative environments, turning complex data into actionable insights, and delivering end-to-end ML solutions—from model development to deployment and observability. I’m passionate about clean code, robust architectures, and continuous learning.…I’m Suyash Naresh Verma, a data science and AI engineering professional pursuing an MS at Arizona State University. I enjoy building scalable ML systems, LLM-powered applications, and data processing pipelines, with hands-on experience across academia and industry. I thrive in collaborative environments, turning complex data into actionable insights, and delivering end-to-end ML solutions—from model development to deployment and observability. I’m passionate about clean code, robust architectures, and continuous learning.

Available to hire

I’m Suyash Naresh Verma, a data science and AI engineering professional pursuing an MS at Arizona State University. I enjoy building scalable ML systems, LLM-powered applications, and data processing pipelines, with hands-on experience across academia and industry.

I thrive in collaborative environments, turning complex data into actionable insights, and delivering end-to-end ML solutions—from model development to deployment and observability. I’m passionate about clean code, robust architectures, and continuous learning.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI Engineer Intern at CloudPlay Solutions

October 1, 2023 - March 1, 2024

Engineered a retrieval-augmented AI assistant using LangChain and vector databases, automating 80% of customer queries and reducing manual workload. Built FastAPI-based ML microservices enabling secure, low-latency access to document-processing and data-aggregation models. Designed anomaly detection pipelines improving event precision by 22% across streaming sources used in operational reporting. Automated model retraining workflows on AWS increasing accuracy consistency by 30% across production iterations.

AI Developer Intern at EasyBee AI, Inc.

June 1, 2025 - August 1, 2025

Improved AI workflow throughput by 50% by redesigning semantic caching and optimizing vector retrieval for document-processing pipelines. Built and deployed React + TypeScript frontend modules enabling users to manage structured/unstructured data sources used for automated report generation. Increased system reliability by 35% by integrating distributed event logging, telemetry, and backend error-recovery mechanisms. Cut release cycles from 3 days to 8 hours by containerizing ML services (Docker) and automating CI/CD deployment on AWS.