# Suyash Bhogawar > Applied AI Transformation that Drives Impact | Stanford Location: San Francisco Bay Area, United States Profile: https://flows.cv/suyashbhogawar 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 & 𝐌𝐋𝐎𝐩𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 Building and scaling production-grade autonomous systems, agentic workflows, and high-performance ML infrastructure. Background in developing massive-scale data pipelines for Stanford and Cornell research labs , now architecting enterprise generative AI ecosystems on AWS, GCP, and Azure. 𝐂𝐨𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐅𝐨𝐜𝐮𝐬: 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐆𝐞𝐧𝐀𝐈 & 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐠𝐞𝐧𝐭𝐬: Engineering multi-agent systems and RAG (Retrieval-Augmented Generation) that drive deterministic execution at scale. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐌𝐋𝐎𝐩𝐬 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: Designing reproducible, secure, and scalable cloud architectures using Terraform and Infrastructure as Code (IaC). 𝐃𝐚𝐭𝐚 𝐒𝐜𝐚𝐥𝐞 & 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Deep foundation in handling sensitive, massive-scale scientific datasets and translating experimental AI models into secure production environments. 𝐒𝐭𝐚𝐭𝐮𝐬: 𝐆𝐫𝐞𝐞𝐧 𝐂𝐚𝐫𝐝 𝐇𝐨𝐥𝐝𝐞𝐫 ℎ𝑡𝑡𝑝𝑠://𝑦𝑜𝑢𝑡𝑢.𝑏𝑒/𝑤𝑜𝐵𝐽𝐸𝑚𝐴𝑌𝐶𝑛𝐼?𝑡=31 ## Work Experience ### Principal Forward Deployed Engineer- Applied AI @ Rackspace Technology Jan 2025 – Present | San Francisco Bay Area • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Engineered and deployed a massive-scale multi-agent browser automation system on AWS. Architected workflow orchestration to handle complex document processing and form submissions autonomously, driving a 10x capacity improvement. • 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 & 𝗔𝗪𝗦 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻: This architecture was selected and showcased by Amazon's SVP of AGI (Rohit Prasad) at the AWS Summit as the defining enterprise case study for Amazon Nova Act. • 𝗦𝘁𝗮𝗰𝗸: Python, AWS (AGI Labs, Lambda, Step Functions, Bedrock), Amazon Nova Act, LangChain. ### Lead Solution Architect - Gen AI @ Rackspace Technology Jan 2023 – Present | San Jose, CA • 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗠𝗟𝗢𝗽𝘀 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 ($𝟱𝟬𝟬𝗕+ 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼): Architected and deployed an end-to-end MLOps platform supporting 300+ data scientists and powering real-time risk analytics for a $500B+ lending portfolio. • 𝗦𝗰𝗮𝗹𝗲 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Built reproducible infrastructure (Sagemaker, ECS, Terraform) with automated CI/CD and model governance, achieving a 40% faster model deployment rate. • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Developed a multi-agent solution to automate code refactoring workflows, yielding a 70% improvement in developer productivity. • 𝗦𝘁𝗮𝗰𝗸: Python, Terraform, AWS (SageMaker, S3, ECS, Bedrock), MLflow, Jenkins ### Senior Solutions Architect - Data Science @ Rackspace Technology Jan 2022 – Present | San Francisco Bay Area MLOps, Distributed Computing, Production ML Systems ### Solutions Architect @ Rackspace Technology Jan 2020 – Jan 2025 | San Francisco Bay Area ### Senior Software Engineer - ML @ NCIRE - The Veterans Health Research Institute Jan 2018 – Jan 2020 | San Francisco Bay Area • 𝗠𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀: Re-architected core analysis pipelines to serve decentralized, multi-institution environments across 60+ sites, driving a 300% increase in platform adoption. • 𝗛𝗶𝗴𝗵-𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Engineered custom machine learning models for complex, high-resolution image segmentation, improving model accuracy by 30% in highly constrained data environments. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗤𝗖): Architected and deployed an automated QC platform that eliminated manual processing bottlenecks, accelerating expert review workflows by 30%. ### Research Data Engineer - Stanford University @ Stanford University Jan 2016 – Jan 2018 | Stanford, CA 𝗚𝗹𝗼𝗯𝗮𝗹 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗢𝗽𝗲𝗻𝗡𝗲𝘂𝗿𝗼): Engineered the data curation architecture for a global data-sharing platform, enabling the automated ingestion, standardization, and publication of 1,500+ massive-scale datasets. • 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 & 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗜𝗺𝗽𝗮𝗰𝘁 ($𝟯𝟬𝗠+): Led the development of open-source data standards, creating a deterministic, reproducible framework that drove $30M+ in institutional savings and eliminated data-sharing friction worldwide. ### Software Engineer @ Cornell University Jan 2014 – Jan 2016 | Tompkins County, NY 𝗖𝗹𝗼𝘂𝗱 𝗠𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲: Architected scalable cloud infrastructure (AWS, HPC clusters) serving 20+ distributed labs. Deployed automated ML pipelines that slashed complex data processing latency by 60%. • 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Established an institution-wide data governance framework enforcing absolute HIPAA compliance and research reproducibility across high-volume, sensitive data environments. ### Research Engineer - Medical Imaging @ Philips Research North-America Jan 2013 – Jan 2014 - Curated DCE-MR Images of cancer patients obtained from multiple institutions/clinical trials - Analyzed 3D MR Images (Feature extraction) using MATLAB - Applied Machine learning techniques for feature selection and classification - Implemented Univariate and Multivariate analysis of image based feature for breast cancer therapy - Developed a pipeline from image acquisition to its classification for prognosis of breast cancer therapy - Optimized Machine learning algorithm for classification using R ### Research Assistant @ George Washington University Jan 2013 – Jan 2013 - Explored diagnostic technique for oral cancer using OCT (Optical Coherence Tomography) - Analyzed OCT images using MATLAB - Fabricated hardware for phase sensitive Optical Coherence Elastography (OCT) ### Intern @ Siemens Healthcare Jan 2011 – Jan 2011 Medical Imaging ## Education ### Master of Science (M.S.) in Biomedical/Medical Engineering The George Washington University ### Bachelor of Engineering (B.E.) in Electrical, Electronics and Communications Engineering Sardar Patel Institute of Technology, Andheri(W); Mumbai ## Contact & Social - LinkedIn: https://linkedin.com/in/suyashb --- Source: https://flows.cv/suyashbhogawar JSON Resume: https://flows.cv/suyashbhogawar/resume.json Last updated: 2026-04-12