AI Solution Engineer

Key Responsibilities

• 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.

• 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.