Nitish Chander Reddy Arjula

About Nitish Chander Reddy Arjula

Software Engineer specializing in Generative AI, cloud-native architecture, and distributed systems, with 3+ years of experience building scalable, production-grade applications across enterprise environments. I design and deploy AI-powered solutions using LLMs, RAG pipelines, and event-driven architectures that translate complex technologies into measurable business outcomes.At Deloitte, I’ve engineered high-performance microservices supporting 10K+ concurrent users, built real-time streaming systems processing 8M+ IoT events daily, and developed Generative AI platforms using OpenAI APIs, LangChain, and Pinecone to reduce enterprise resolution times by 25%. I’ve led cloud migrations to Azure that delivered $45K+ in annual savings and implemented Infrastructure as Code (Terraform, Bicep) to reduce deployment time by 70% while ensuring SOC 2–aligned security.Previously at Wipro, I built full-stack applications using React and Python, optimized databases for sub-second performance, containerized legacy systems with Docker, and productionized ML models into scalable APIs.

Experience

1. Software Engineer

   Deloitte

   Aug 2024 — Present · US

   Engineered scalable microservices within a distributed systems architecture using Flask, FastAPI and asyncio, delivering secure RESTful APIs over HTTPS that improved response times by 40% while supporting 10K+ concurrent users.• Designed and deployed Generative AI solutions using Hugging Face Transformers, OpenAI APIs, and RAG pipelines (LangChain, Pinecone), reducing enterprise support resolution time by 25% and enabling intelligent knowledge retrieval.• Led migration of a legacy C#.NET application to a cloud-native Azure architecture with Azure Functions and Azure Cosmos DB, achieving $45K in annual infrastructure cost savings and improved system scalability.• Built a real-time event-driven architecture using Apache Kafka and Spark Streaming, processing 8M+ IoT events per day with 99.98% uptime and low-latency ingestion.• Automated CI/CD pipelines using Azure DevOps and GitHub Actions, integrating Pytest to reduce production deployment failures by 90% and accelerate release cycles.• Implemented Infrastructure as Code (Terraform, Azure Bicep) across multi-region Azure environments, cutting deployment time by 70% while ensuring SOC 2–aligned security and compliance.

Education

• University at Albany

  Master's degree, Computer Science