Nishant Manchanda

University of California, Irvine, CA

Graduated: June 2016

Master of Science in Engineering Management

Henry Samuel School of Engineering and Paul Merage School of Business

Microsoft Corp, Redmond, WA

July 2023 - Present

Engineering Manager – Level 64

• Leading a Team of 7 Software Engineers delivering Cloud and AI Services for Devices Supply Chain in Both Commercial and Federal Clouds
• Leading the Team in building scalable Agentic AI Systems within Devices Supply Chain Team
• Guided Team to build Agents to query Enterprise Confidential Data using Semantic Kernel
• Improved accuracy of NL2SQL Query from 50% to >90% using Prompt Engineering and Azure AI Semantic Re-ranker Search
• Created optimized caching layer to cache similar queries and reduce LLM querying cost
• Created a unified Authorization Layer for Declarative Copilot
• Led team for SFI (Microsoft Secure Future Initiative) Wave 1 and 2 completion
• Drove On-Time Execution and Delivery of 500+ KPI's for SFI, achieving 95% completion
• Transformed Supply Chain Services Architecture to reduce infrastructure cost by $4.8 Million/year via Zero Trust Architecture
• Increased GitHub Copilot Adoption from 20% to 75% with a 30% Code Acceptance Rate
• Leading the Digital Resiliency Workstream for Microsoft Devices Operations
• Led the team in delivering a federal cloud supply chain management solution
• Core Technologies: Azure Identity Management, Azure Networking Resources, Bicep Templates
• Impact: Successful Implementation of Manufacturing Services in Cloud Supply chain and $1M YoY cost saving compared to On-Prem Infrastructure

Microsoft Corp, Redmond, WA

April 2022 - July 2023

Senior Software Engineer – Level 63

• Led Solution Architecture for Manufacturing Cloud Native Technologies, building a secure and scalable digital supply chain for manufacturing Microsoft Devices
• Conceptualized and delivered a scalable MLOPS Infrastructure for internal Data Science Engineering team to build, deploy and manage machine learning models for Commercial Fraud Detection
• Built Real-Time Inferencing Endpoints using Azure Machine Learning Studio for near real-time fraud detection
• Built Automated Pipelines for daily Model Training on Labelled Data from Fraud investigators, expediting deployment by 75%
• Built a cloud-native Configuration Management Service for managing manufacturing limits in compliance with the Manufacturing Change Review process
• Created a software-based Disaster Recovery/Load Balancer service to replace expensive F5 Load Balancers using a Round-Robin algorithm
• Core Technologies: Python, YAML Development, Azure Machine Learning Studio, Azure App Service, Blazor Front End, Microservice architecture for Backend (Azure Durable Functions), Database (Azure Cosmos DB)
• Impact: $250K saved in the first 2 months of operations, 90% Reduction in Manufacturing Limits Deployment Time from 3 Days to 4 Hours, Saved the organization $250K YoY

Microsoft Corp, Redmond, WA

August 2021 - April 2022

Software Development Engineer – Level 62

• Worked in Azure IoT Edge Team, building optimized rust-based telemetry applications (50% faster and 33% smaller footprint)
• Designed and Implemented a dynamically configurable MQTT Based Metrics Collector for exporting metrics to Azure IoT Hub's Log Analytics workspace from IoT Edge Devices
• Designed and Implemented a First Class Validation Toolkit for certifying IoT Edge eligibility on custom OS/Hardware
• Built Automated Integration Test Pipelines, reducing new Bugs and Regression by 30%
• Technologies Used: RUST, Bash, GitHub Code spaces, GitHub Actions, YAML Pipelines

Microsoft Corp, Redmond, WA

February 2019 - August 2021

Software Development Engineer 2 – Level 61

• Led software development of Manufacturing Test Hardware for Microsoft Products (HoloLens, Laptops, and Xbox)
• Developed Common .NET Core Libraries for Secure Bearer Token-based communication with Cloud Services for pushing Manufacturing Data to the cloud
• Software Development using C# for Automated Robot Based Testing, including HTTP Based Secure Communication between Factory Robot and Tester and TCP/IP Socket Based Communication to control Tester GPIO's
• Developed Re-usable Libraries for Hardware control in factories (Sensors, RF Equipment, Audio Testing Equipment), enabling faster scalability and better code maintainability
• Implemented 90% Unit Test Coverage of Code and Developed Integration Test Pipelines using Azure DevOps for Automated Testing and Release of Production Code
• Technologies Used: C#