The AI Solutions Engineer is responsible for the development, integration, implementation, and maintenance of artificial intelligence (AI) and large language model (LLM) applications used to support internal business operations. This position develops AI-enabled applications, integrations, and tools and connects AI capabilities with existing enterprise systems and data sources.
The position works with business and technology teams to translate functional requirements into technical solutions and supports AI applications throughout the development lifecycle, including design, development, testing, deployment, maintenance, and enhancement. The AI Solutions Engineer also ensures solutions are developed in accordance with applicable information security, privacy, data governance, and technology standards.
AI and Agentic Solution Development
- Design, develop, test, deploy, and maintain LLM-powered applications, integrations, and agentic AI solutions using Azure cloud technologies, Azure OpenAI, and related AI services.
- Develop Python-based applications and services that support natural language processing, generative AI, and other AI-enabled business use cases.
- Design and implement agentic workflows that orchestrate AI services, models, tools, and internal or external data sources to address defined business requirements.
- Develop and implement retrieval-augmented generation (RAG), prompt engineering, semantic search, embeddings, and related techniques to support AI application functionality.
- Develop reusable application components, services, and technical patterns to support AI capabilities across internal applications.
- Develop prototypes and proofs of concept and transition approved solutions into production applications.
AI Integration, Performance and Optimization
- Integrate Azure OpenAI APIs, AI services, and external models with internal applications, systems, and data sources.
- Design and maintain integrations with consideration for application reliability, scalability, performance, and maintainability.
- Develop and optimize prompts, model configurations, and parameters to improve application performance, response quality, and cost efficiency.
- Perform model configuration and fine-tuning, as appropriate, based on application requirements and available technologies.
- Monitor, troubleshoot, and debug agentic workflows, application integrations, and AI-generated outputs.
- Evaluate changes in LLM and AI technologies and recommend modifications or enhancements to existing applications and technical approaches.
AI Monitoring, Security & Reliability
- Develop and implement automated evaluation methods and frameworks to monitor the quality, accuracy, reliability, and performance of AI-enabled applications.