Shreyansh Joshi

Software Engineer

Seattle, Washington, United States2 yrs 3 mos experience
AI EnabledAI ML Practitioner

Key Highlights

  • Expert in building scalable AI systems.
  • Proven track record in cloud computing solutions.
  • Strong background in full-stack development.
Stackforce AI infers this person is a Cloud Computing and AI specialist with a strong focus on scalable systems.

Contact

joshi@cs.cmu.eduLinkedIn

Skills

Core Skills

Software DesignAmazon Web Services (aws)Artificial Intelligence (ai)Software ArchitectureApi DevelopmentSystem On A Chip (soc)Process AutomationCloud ComputingComputer Vision

Other Skills

Back-End Web DevelopmentBashC++Cloud ApplicationsCloud InfrastructureCompetitive ProgrammingComputer ArchitectureComputer ScienceContainerizationContinuous Integration and Continuous Delivery (CI/CD)Database DesignDatabasesDeep LearningDesignDistributed Systems

About

I'm a software engineer currently working at Amazon in the AGI Infra organization. I’m part of the Data team, where I worked on creating a scalable system to move large-scale training data with minimal latency and manual effort, enabling cutting-edge GenAI research by Amazon scientists. I am currently working on a distributed, in-memory checkpointing solution for training jobs that reduces the read and write latency of checkpoints, enabling faster training and lower compute utilization. Previously, I worked at Microsoft on Azure’s developer-facing clients (CLI, PowerShell, and Portal), gaining experience across the full stack of cloud services and user interaction. My work spanned performance optimization, service integration, and delivering seamless experiences to millions of developers. I hold a Master’s degree in Computer Science from UC San Diego, where I specialized in AI, and completed my undergraduate studies at BITS Pilani. My 2024 internship at Qualcomm helped me grow as a systems-oriented engineer, working on SoC-level software and deepening my understanding of hardware-software co-design. I'm passionate about building intelligent, scalable systems at the intersection of AI and cloud computing. My goal is to continue developing products that empower AI innovation and deliver real-world impact at scale.

Experience

2 yrs 3 mos
Total Experience
1 yr 1 mo
Average Tenure
1 yr 2 mos
Current Experience

Amazon

Software Engineer

Apr 2025 – Present · 1 yr 2 mos · Bellevue, Washington, United States · On-site

  • Developed a scalable, high-throughput data transfer service enabling efficient movement of large-scale GenAI training data across S3 buckets (in-region and cross-region), with the goal of reducing manual effort by up to 70%.
  • Currently working on a distributed, in-memory checkpointing solution for training jobs to reduce the read and write latency of checkpoints, thereby accelerating training and lowering compute wastage
Software DesignAmazon Web Services (AWS)

Qualcomm

Systems Software Engineer Intern

Jun 2024 – Sep 2024 · 3 mos · San Diego, California, United States · On-site

  • Engineered a voltage profiling tool (for the Systems Performance Team (SPT)) to automate and streamline the evaluation of critical system features across system-on-chip (SoC) subsystems such as GPU, CPU, NPU, reducing testing time by 15 hours/week
  • Streamlined data management by integrating characterization outputs into a MySQL database, seamlessly connecting it to a PowerBI dashboard, enabling real-time data visualization
  • [UPD] As of a year after my internship, this tool has been widely adopted across various SPT teams to characterize their voltage and visualize the outputs
Programming LanguagesMachine ToolsSystem on a Chip (SoC)GitComputer ScienceMySQL+10

Uc san diego

2 roles

Graduate Student Researcher

Promoted

Feb 2024 – Mar 2025 · 1 yr 1 mo · San Diego, California, United States

  • Designed scalable ML models leveraging time-series hospital data for early detection of diseases like sepsis, COVID-19 & influenza and deploying these models on a federated medical AI platform: MedPerf using MLCube containers to autonomously monitor hospital data while preserving patient privacy through decentralized computation
  • Led the development of an end-to-end software solution for QuaLLM: A SEP1 automation project, comprising multiple systems to streamline sepsis compliance reporting from UCSD Health to the Centers for Medicare & Medicaid Services (CMS)
Programming LanguagesLarge Language Models (LLM)Artificial Intelligence (AI)PyTorchWeb ApplicationsComputer Science+9

Graduate Teaching Assistant

Jan 2024 – Mar 2024 · 2 mos · San Diego, California, United States

  • Teaching Assistant for the course MGTA 455: Customer Analytics and Generative AI, under Prof. Vincent Nijs
Next.jsProgramming LanguagesComputer ScienceContainerization

Microsoft

3 roles

Software Engineer

Jul 2022 – Aug 2023 · 1 yr 1 mo

  • Engineered a config-driven platform on Azure Portal, providing customers a unified view of Azure Business Continuity and Disaster Recovery (BCDR) activities
  • Improved scalability by automating integration with 3rd-party DR solutions like Commvault, Rubrik to handle more workloads
  • Implemented VM disk and NIC renaming in Azure Portal, achieving a user-reliability of 99.87% across 1000+ enterprise users
Programming LanguagesTestingNode.jsReact.jsGitWeb Applications+20

Software Engineer Intern

Feb 2022 – Jun 2022 · 4 mos

  • • Worked on building a single window for disaster recovery solutions in the Azure BCDR team. Designed highly reusable React components (that were even used by other teams for their purposes) and took complete ownership of certain views of our platform (jobs, protected items, inventory), driving them to completion.
Programming LanguagesTestingReact.jsGitWeb ApplicationsComputer Science+10

Software Engineer Intern

May 2021 – Jul 2021 · 2 mos · Hyderabad, Telangana, India

  • Introduced Archive feature into Azure Command Line Interface (CLI) for various Azure Backup workloads such as IaasVM, SQL, SAP HANA. This allows customers to backup their data for extended periods at up to 87% lower costs using CLI as a client.
  • Automated and scaled testing by developing Powershell unit tests for the CLI codebase and later onboarding them to cloud
Programming LanguagesOpen-Source DevelopmentGitWeb ApplicationsComputer ScienceSoftware Design+11

Silver touch technologies ltd

Machine Learning / Deep Learning Intern

May 2020 – Jun 2020 · 1 mo

  • Developed a comprehensive end-to-end Deep Convolutional Neural Network (DCNN) for facial demographics analysis, designed to predict individual age and gender from facial images, achieving a 94.94% accuracy in gender classification and a Mean Absolute Error (MAE) of 4.58 for age estimation.
  • Successfully implemented and deployed the model on a local server using Flask and published the work titled “Age and Gender Prediction Using Deep CNNs and Transfer Learning” published in Springer CCIS.
Programming LanguagesComputer VisionArtificial Intelligence (AI)Facial RecognitionComputer ScienceFine Tuning+5

Education

UC San Diego

Master of Science - MS — Computer Science

Sep 2023 – Jun 2025

Birla Institute of Technology and Science, Pilani - Goa Campus

Bachelor of Technology - B.Tech — Computer Science

Jan 2018 – Jan 2022