AI Solution Engineer (607)

AI Solution Engineer

Organization: Central AI & Data Observability (AIDO)

Team: AI Platform Strategy & Services

Location: Hybrid – Herndon, VA

Role Overview

As an AI Solution Engineer, you will operate at the intersection of business strategy and hands-on engineering. As a key member of the AI & Data Observability (AIDO) team, you will help Lines of Business (LOBs) become AI-enabled by identifying, designing, and implementing innovative AI solutions.

This is a dual-role position that combines strategic consulting and technical development. You will work directly with business stakeholders to identify high-value AI opportunities while also building Proof of Concepts (POCs) on the enterprise AI platform. Your efforts will help reduce operational effort, improve business outcomes, and ensure solutions are built with strong observability and data integrity standards.

Key Responsibilities1. Technical Consultancy (Strategy)

• Discovery & Assessment: Partner with Lines of Business to analyze existing business processes, gather requirements, and identify opportunities where AI and automation can significantly reduce manual effort.
• Solution Design: Translate high-level business requirements into practical, scalable technical solutions leveraging the AIDO Hub-and-Spoke platform architecture.
• Value Engineering: Develop AI Value Scorecards to estimate return on investment (ROI) and reductions in level of effort (LOE) for proposed use cases.
• Center of Excellence: Guide business teams in applying AI use cases using existing and planned AIDO platform capabilities while promoting adoption of Generative AI best practices and methodologies.

2. Rapid Prototyping & Development (Builder)

• Platform Mastery: Maintain a current development environment connected to AI platform APIs and UI services to stay ahead of new platform capabilities and releases.
• Informal POCs: Build rapid prototypes and perform technical validation in sandbox environments utilizing AWS OpenSearch, Retrieval-Augmented Generation (RAG) patterns, and Large Language Model (LLM) orchestration.