AI Solution Engineer

Key Responsibilities

1. Technical Consultancy (The "Strategy" Hat)
Discovery & Assessment: Partner with LOBs to map existing business processes, capture requirements, and identify friction points where AI/Automation can significantly reduce manual effort.
Solution Design: Translate vague business requirements into concrete technical solutions that leverage the Hub-and-Spoke AIDO Platform.
Value Engineering: Develop "AI Value Scorecards" to estimate potential ROI and LOE reduction for proposed use cases.
Center of Excellence: Assist LOBs with the application of AI use cases with existing and planned AIDO Platform capability. Drive adoption of Generative AI principles and techniques.
2. Rapid Prototyping & Development (The "Builder" Hat)
Platform Mastery: Maintain a current development environment against the AI Platform’s APIs and UI services to stay ahead of new feature releases.
Informal POCs: Conduct rapid technical validation by building functional prototypes in a sandbox environment (utilizing AWS OpenSearch, RAG patterns, and LLM orchestration).
Technical Feasibility: Perform "smoke tests" on LOB data to ensure it is viable for the proposed AI application before full-scale development begins.
Reference Implementation: Build and maintain a library of "Golden Path" code samples and templates to accelerate LOB developers.

Required Qualifications
Education: Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
Technical Proficiency: * Experience with Python or JavaScript and interacting with RESTful APIs.
Familiarity with cloud-based data environments (e.g., AWS OpenSearch, S3).
Understanding of Generative AI concepts (Language Models, Prompt Engineering, RAG, Other data techniques to support AI).
Analytical Mindset: Ability to deconstruct complex business workflows into logical steps for automation.
Communication: Strong ability to explain technical AI concepts to non-technical stakeholders in a persuasive, consultative manner.