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.

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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.