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The AI Engineer role focuses on developing and supporting GenAI applications using Python and Core Java. Candidates should have experience with model deployment, data integration, and prompt engineering, particularly in Azure cloud services. This position offers the opportunity to work on innovative projects in a collaborative environment.
Job Responsibility:
Specialized role focused on supporting, designing, developing GenAI applications
Provides high touch service to Hanover users regarding changes in-scope of the support services
Supports the GenAI applications including model deployment, configuration, data integration, architecture support, prompt engineering, issue resolution, and regression testing
Translates support issues into designs and develops the GenAI solutions, integrations, and prompt engineering
Utilizes core development skills in tools such as python, API builds and integrations, Gen AI patterns such as RAG and others, Azure cloud services and is responsible for delivering upon the design
Alternate between core development of GenAI services (e.g., LLM orchestration, embedding optimization, tool/function calling logic) and support of production GenAI applications (e.g., incident resolution, service monitoring, and root cause analysis for production deployments)
Implement, and support RAG-based GenAI solutions integrating vector stores, chunking strategies, prompt pipelines, and semantic search frameworks (e.g., Semantic Kernel, Azure AI Search)
Write and maintain clean, modular Python code for GenAI application such as LLM integration, data preprocessing, and tool-based agent design
Requirements:
Experience with model deployment, configuration, data integration, architecture support, prompt engineering, issue resolution, and regression testing
Experience translating support issues into designs and developing GenAI solutions, integrations, and prompt engineering
Core development skills in tools such as python, API builds and integrations, Gen AI patterns such as RAG and others, Azure cloud services
Working knowledge of Gen AI frameworks (Ie. Semantic kernel)
Experience with core development of GenAI services (e.g., LLM orchestration, embedding optimization, tool/function calling logic) and support of production GenAI applications (e.g., incident resolution, service monitoring, and root cause analysis for production deployments)
Experience implementing and supporting RAG-based GenAI solutions integrating vector stores, chunking strategies, prompt pipelines, and semantic search frameworks (e.g., Semantic Kernel, Azure AI Search)
Ability to write and maintain clean, modular Python code for GenAI application such as LLM integration, data preprocessing, and tool-based agent design