Anup Rai

Engineer 1

Wipfli

08.2022 - Current

Architected and deployed production-grade AI systems using Python, OpenCV, and deep CNN models (ResNet, YOLO, U-Net) for defense-critical applications, achieving 95%+ accuracy in real-time processing
Led cross-functional AI development team of 6 engineers, implementing Agile methodologies with sprint planning, daily standups, and code reviews, resulting in 40% faster delivery cycles
Developed end-to-end computer vision pipelines for medical X-ray image enhancement using TensorFlow, implementing GANs (Real-ESRGAN, Pix2Pix) and traditional CV algorithms for noise reduction, contrast enhancement, and automated anomaly detection
Engineered LLM-powered intelligent systems using transformer models (BERT, GPT variants) with RAG (Retrieval-Augmented Generation) architecture for automated document processing, achieving 90% accuracy in query answering, with sub-2 second response times.es
Developed scalable .NET Core 6/7 Web APIs with clean architecture patterns (CQPR, Repository, Dependency Injection), implementing JWT authentication, role-based authorization, and Entity Framework Core for data persistence
Built microservices architecture using .NET Core with Docker containerization, implementing service-to-service communication via gRPC and message queues (RabbitMQ/Azure Service Bus) for high-throughput AI processing workflows
Integrated AI/ML models into .NET Core applications using ML.NET and ONNX Runtime, enabling real-time inference capabilities with 99.9% uptime and horizontal scaling support
Implemented RESTful APIs and GraphQL endpoints in .NET Core for AI model serving, with comprehensive input validation, custom middleware for request/response logging, and automated API documentation using Swagger/OpenAPI
Established comprehensive CI/CD pipelines using Azure DevOps/GitHub Actions with automated testing (unit, integration, performance), Docker containerization, and blue-green deployment strategies for zero-downtime releases
Implemented robust testing frameworks with 90%+ code coverage using NUnit, MSTest for .NET applications and pytest for Python ML pipelines, including mock services and integration test suites
Developed automation scripts and tools for Green 19 desktop application using PowerShell, Python, and .NET Core Console applications, reducing manual testing time by 60% and improving deployment reliability
Configured monitoring and observability solutions using Application Insights, Prometheus, and custom logging frameworks, implementing alerting systems for real-time performance tracking and anomaly detection
Optimized ML model performance through quantization, pruning, and TensorRT optimization, achieving 3x faster inference speeds while maintaining model accuracy for production deployments
Implemented hybrid cloud solutions integrating on-premises .NET Core applications with Azure AI services (Cognitive Services, ML Studio), ensuring secure data processing for sensitive defense applications
Developed custom NuGet packages and Python libraries for reusable AI/CV components, implementing versioning strategies and automated package deployment to internal repositories

Summary

Overview

Work History

Engineer 1

Education

B.E - Electronics And Communication

CLASS XII - Karnataka State Board

Skills

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Timeline

Engineer 1

B.E - Electronics And Communication

CLASS XII - Karnataka State Board

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