AI Engineer - Information Technology

Position Overview

Pond is seeking an AI Engineer to support the design and deployment of AI-enabled applications that enhance our technical and business operations. This role focuses on implementing modern AI capabilities within enterprise systems and contributing to scalable solutions that improve knowledge access, digital workflows, and operational effectiveness.

Key Responsibilities

• Develop & Implement: Build and maintain AI-enabled enterprise applications under the guidance of senior engineers and technical leadership.
• Integrate AI Services: Implement integrations with large language model (LLM) APIs and related AI services within internal tools and workflows.
• Support RAG Systems: Contribute to the development and optimization of retrieval-augmented generation (RAG) systems using structured and unstructured enterprise data.
• Translate Requirements: Collaborate with cross-functional stakeholders to translate defined business and technical requirements into reliable AI-driven features and enhancements.
• Ensure Quality & Compliance: Follow established governance, security, and performance standards to ensure AI implementations are reliable and compliant.
• Collaborate Cross-Functionally: Work closely with software engineers, operational leaders, and AEC domain experts to support execution of Pond’s enterprise AI initiatives.

Required Qualifications

• 3+ years of professional software engineering experience.
• Proficiency in Python and/or C#, with experience integrating APIs and external services.
• Hands-on experience working with LLM APIs or similar AI technologies in application development.
• Solid understanding of application architecture, system design principles, and secure development practices.
• Strong problem-solving skills and the ability to communicate technical concepts clearly to cross-functional stakeholders.

Desired Additional Experience

• Experience within architecture, engineering, construction (AEC), or other technically complex industries.
• Exposure to RAG systems, vector databases, or multi-source enterprise data environments.
• Familiarity with enterprise data governance, security best practices, and compliance considerations related to AI solutions.