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# AI Solution Engineer

- **Company:** The Dignify Solutions LLC
- **Location:** Herndon, VA, United States
- **Contract:** Permanent contract
- **Skills:** Reference Implementation, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Database, Integrated Development Environments, Python (Programming Language), Language Modeling, Rapid Prototyping Process, Software Engineering, Value Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, AI Platforms, Information Technology, Restful APIs, OpenSearch
- **Published:** November 14, 2026
- **Apply:** https://www.juju.com/job/16_627a0a09

## About the Role

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.

## Description

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.