Summary: Design and develop enterprise backend applications using .NET or Java, integrating Generative AI capabilities like LLMs, RAG, and Agentic AI into Azure services. The role focuses on building AI-powered workflows, REST APIs, and microservices while ensuring scalability and security.

AI / Generative AI Engineer – .NET / Java (Agentic AI & Azure AI)

Experience: 8-9 Years

Location: Hyderabad / Visakhapatnam – Hybrid (WFO)

Work Timings: Australia Shift : 7 AM - 4 PM IST

Job Summary

We are looking for a hands-on AI / Generative AI Engineer with a strong background in .NET (C#) or Java backend development and practical experience integrating Generative AI capabilities into enterprise applications.

The ideal candidate should have experience building AI-powered applications using LLMs, Retrieval-Augmented Generation (RAG), Agentic AI concepts, REST APIs, and Azure AI services.

The candidate should be comfortable developing enterprise-grade backend services using .NET or Java and integrating them with Azure OpenAI, AI Foundry, vector databases, and other AI orchestration frameworks.

This role is primarily focused on .NET / Java application development with Generative AI integration. Python-only AI/ML developer profiles are not the target for this position.

Key Responsibilities

Design, develop, and maintain enterprise backend applications using .NET (C# / ASP.NET Core) or Java (Spring Boot).
Integrate Large Language Models (LLMs) into enterprise applications using Azure OpenAI and other LLM providers.
Develop Generative AI applications incorporating RAG (Retrieval-Augmented Generation) architectures.
Build AI-powered workflows and Agentic applications involving tool calling, function calling, orchestration, and multi-step reasoning.
Work with Azure AI Foundry and Azure AI services to build and deploy AI-enabled solutions.
Integrate LLMs with enterprise data sources using vector databases and Azure AI Search.
Work with Azure Cosmos DB and other databases for application data and AI-related use cases.
Develop and integrate REST APIs, microservices, and backend services to support AI-powered applications.
Implement document processing, embedding generation, semantic search, and retrieval pipelines.
Explore and implement AI orchestration frameworks such as Semantic Kernel, LangChain, and LangGraph.
Integrate AI workflows with enterprise applications, automation platforms, APIs, and webhooks.
Optimize application performance, scalability, security, and response times.
Implement responsible AI practices, including data privacy, access controls, and governance.
Collaborate with product, data, and engineering teams to translate business requirements into practical AI solutions.

Required Skills & Qualifications

Backend Development – Mandatory

Strong hands-on experience in either .NET (C#, ASP.NET Core, Web API) or Java (Spring Boot, REST APIs).
Good understanding of OOP concepts, design patterns, microservices, and API development.
Experience developing and integrating enterprise applications and backend services.
Strong knowledge of REST APIs, authentication, authorization, and database integration.

Generative AI / LLM – Mandatory

Practical experience integrating LLMs into applications using Azure OpenAI or OpenAI APIs.
Understanding of Generative AI concepts, prompt engineering, and LLM-based application development.
Hands-on experience or implementation exposure to RAG architectures.
Understanding of embeddings, vector search, semantic search, and document retrieval.
Experience building AI-powered workflows involving tool calling, function calling, or agent-based interactions.

Azure AI & Data Technologies

Working experience with Azure AI Foundry and/or Azure OpenAI.
Exposure to Azure AI Search or vector databases such as FAISS, Pinecone, Weaviate, or Chroma.
Working knowledge of Azure Cosmos DB or other NoSQL databases.
Understanding of integrating AI services with enterprise data sources.

Good to Have

Experience with Microsoft Semantic Kernel, particularly for .NET-based AI applications.
Exposure to LangChain and LangGraph.
Experience with multi-agent architectures and agent orchestration.
Knowledge of Azure Machine Learning.
Experience with LLMOps, MLOps, and AI application monitoring.
Exposure to Docker, Kubernetes, and CI/CD pipelines.
Experience with document intelligence, data lakes, and structured/unstructured data processing.
Familiarity with automation platforms, webhooks, and SaaS integrations.
Experience with production AI application deployment and performance optimization.

What We Are Looking For

Developers with a strong .NET or Java backend engineering foundation.
Candidates who have implemented Generative AI features in enterprise applications.
Hands-on experience with LLM integrations, RAG, and Azure AI services.
Developers who can independently build APIs, integrate AI services, and troubleshoot application-level issues.
Practical implementation experience rather than purely theoretical AI/ML knowledge.

What We Are NOT Looking For

Python-only developers without strong .NET or Java development experience.
Pure Data Scientists or Machine Learning Engineers focused primarily on model training.
Candidates with only theoretical knowledge of Generative AI and LLM concepts.
Candidates whose experience is limited to prompt engineering or chatbot configuration without backend development.
Candidates without hands-on enterprise application development experience.