Design and develop scalable backend services and AI integrations using .NET (C#, ASP.NET Core)

Implement tool-calling, memory management, and structured context handling for LLM-driven systems

Build and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases

Develop and orchestrate agentic workflows using frameworks such as Semantic Kernel, LangChain, LlamaIndex, or AutoGen

Apply advanced prompt engineering techniques including ReAct, Chain-of-Thought (CoT), function-calling, and structured outputs

Integrate Azure OpenAI, Gemini, and other LLM APIs into enterprise-grade applications

Develop and manage secure APIs using REST, WebSockets, and implement authentication using OAuth2 and JWT

Implement caching, logging, monitoring, and performance optimization for AI-powered services

Build and deploy cloud-native applications using Microsoft Azure services (App Services, Functions, API Management, Service Bus)

Containerize and deploy applications using Docker and orchestrate using Azure Kubernetes Service (AKS) where required

Develop high-performance and scalable solutions using asynchronous programming, parallel processing, and streaming in .NET

Work extensively with embeddings, vector databases, and Azure Cognitive Search for semantic retrieval

Apply best practices in prompt engineering to improve LLM accuracy, relevance, and reliability

Ensure secure and efficient communication using web protocols such as HTTP, REST, and WebSockets

Implement CI/CD pipelines and DevOps practices using Azure DevOps

Monitor and troubleshoot applications using Azure Monitor, Application Insights, and logging frameworks

Ensure compliance with security, governance, and data privacy standards in AI implementations