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
MOURI Tech