AI Product Engineer

Role Overview

We are seeking an AI Product Engineer (AI-enabled Full-Stack Engineer) to join our internal AI incubator. This unique role bridges the gap between business problems and secure, reusable internal products delivered in days or weeks rather than months.

You will work centrally within our enterprise team, taking prioritized requests from various departments—from initial stakeholder conversations through prototype development, production deployment, and continuous improvement. Unlike traditional roles, this position combines product discovery, engineering, quality ownership, and operations to ensure accountability without fragmented handoffs.

Key Responsibilities

• Full-Stack Engineering: Design and implement products across interfaces, services, and databases from concept through production deployment.
• AI Engineering: Apply AI techniques including Retrieval-Augmented Generation (RAG), agentic workflows, and utilize AI-assisted development tools effectively.
• Integration & Systems: Integrate APIs with existing enterprise line-of-business systems to create seamless solutions.
• Stakeholder Management: Translate ambiguous business requirements into clear technical specifications, bounded scope, and measurable success criteria.
• Quality Ownership: Lead test scenario design, data validation, edge-case handling, load testing coordination (UAT), and defect resolution.
• Deployment & Operations: Manage CI/CD pipelines, maintain source control discipline, monitor applications, create runbooks, and provide post-launch support.

Tech Stack & Skills

.NET Core Expertise: Deep proficiency in C# and ASP.NET Core for building secure, high-performance web APIs with strong attention to dependency management and testability.

Modern Front-End: Build responsive, accessible interfaces using React and TypeScript, implementing robust component design, state management, and form validation patterns.

Detailed Requirements

• Azure & DevOps: Strong fundamentals in Microsoft Azure (App Services, Containers) including secret/key management and pipeline configuration via Azure DevOps or GitHub.
• Database Engineering: Expertise in SQL Server for relational data modeling, stored procedures, query optimization, index tuning, and schema migrations.
• Automation & Testing: Implement comprehensive testing strategies across the stack including unit tests (xUnit/NUnit), integration tests, component tests, end-to-end scenarios, and test data management.