Applied AI Engineer - Python in Hyderabad, भारत

Role Summary : The Applied Engineer will work on building and scaling GenAI-driven conversational applications, with a strong focus on backend development, AI agent orchestration, and cloud-native deployments. This role requires hands-on expertise in Python, .NET, and Azure, and the ability to translate GenAI concepts into reliable, production-grade https://jobeax.com/link/LNO4qyefA3UlJa2l Responsibilities : - Design, develop, and maintain backend services using Python (FastAPI, Flask) and .NET Core (C#)- Build, orchestrate, and optimize GenAI agent workflows using LangChain, LangGraph, AutoGen, or similar frameworks- Develop and expose RESTful APIs for chatbot and backend integrations- Integrate applications with Azure services including Azure OpenAI, CosmosDB, and Application Insights- Collaborate with ML engineers to deploy, monitor, and optimize GenAI agents in production- Implement CI/CD pipelines and support containerized deployments using Docker and Azure DevOps- Debug, test, and document code with a strong emphasis on scalability, security, and maintainability- Participate in code reviews and contribute to engineering best practices- Mentor junior engineers as required and support knowledge sharingRequired Skills : - Strong proficiency in Python, with hands-on experience using FastAPI, Poetry, and pytest- Working knowledge of .NET Core, REST APIs, and backend service architectures- Hands-on experience with GenAI frameworks such as LangChain, LangGraph, AutoGen, or equivalent- Experience with Azure cloud services, particularly Azure OpenAI and CosmosDB- Solid understanding of Git workflows, debugging techniques, and unit testing- Ability to independently understand and work within existing codebases and documentationPreferred / Good-to-Have Skills : - Experience with RAG architectures, vector databases, and prompt engineering- Exposure to CI/CD pipelines, especially Azure DevOps- Familiarity with frontend frameworks such as Angular or React- Prior experience working on customer-facing chatbots, conversational AI, or recommendation systemsKey Outcomes : - Faster delivery of GenAI chatbot features and enhancements- Improved system reliability and production stability- Scalable and maintainable GenAI application architecture- Reduced turnaround time for customer and business-driven changes (ref:hirist.tech)