Applied AI Engineer - Python

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 systems.

Key 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 sharing

Required 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 documentation

Preferred / 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 systems

Key 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