Product Engineering Architect

Since 2012, we've grown to become one of the leading single-family rental companies and homebuilders in the country, recently recognized as a top employer by Fortune and Great Place To Work. At AMH, our goal is to simplify the experience of leasing a home through professional management and maintenance support, so our residents can focus on what really matters to them, wherever they are in life.

The Product Engineering Architect is a strategic and hands-on technical individual responsible for executing the Product Engineering AI architecture vision, designing and delivering production-grade AI solutions, and leading the adoption of modern AI technologies across the organization. Partners with leadership, business stakeholders, and engineering teams to identify high-impact AI use cases, establish architecture frameworks, and ensure AI solutions are scalable, governed, and aligned with organizational goals and requirements.

Leverages deep expertise in Generative AI, Agentic AI systems, LLM orchestration, and MLOps skillsets to translate complex AI capabilities into production systems and reusable components. Collaborates with Compliance, Security, and Architecture teams to ensure responsible AI deployment, regulatory alignment, and enterprise-grade production readiness. Establishes reusable architecture patterns and reference designs, assists in AI platform selection, and mentors engineering and architecture teams to build organizational AI fluency and capability.

Partners with product, engineering, and leadership to conduct proofs of concept, evaluate emerging AI technologies, and contribute to the organization's broader AI capabilities. Defines standards and best practices to enable the organization to scale AI solutions responsibly and effectively in the Product Engineering group.

Responsibilities:

Guides the execution of the Product Engineering AI roadmap, defining architecture standards, reusable frameworks, and scalable AI design patterns aligned with business objectives.

Designs and delivers enterprise-grade AI solutions, including Generative AI applications, Agentic AI systems, Retrieval-Augmented Generation (RAG) pipelines, LLM integrations, and MLOps/LLMOps frameworks.

Partners with business and technology stakeholders to translate goals and requirements into practical, innovative AI solutions.

Evaluates emerging AI technologies, conduct proofs of concept, and recommend solutions that deliver measurable value.

Collaborates with Security, Risk, Compliance, and Architecture teams to support responsible, secure, and compliant AI implementations.

Assesses solution risks, recommends mitigation strategies, and contributes to governance practices that support enterprise AI initiatives.

Mentors engineers and architects, sharing best practices and fostering a culture of innovation, learning, and continuous improvement.

Develops reference architectures and reusable components that promote consistency, scalability, and operational excellence across projects.

Requirements:

Bachelor's degree in Computer Science, Engineering, Statistics, Data Science, or a related field or an equivalent combination of education and experience required;
Master’s degree preferred.

Minimum 8 years of experience in solution architecture, technical design, and enterprise technology delivery.

Minimum 3 years of Strong knowledge of Generative AI, Large Language Models (LLMs), Agentic AI systems, and AI operational frameworks preferred

Experience leading technical initiatives and architecting complex solutions, particularly within regulated or highly governed environments.

Experience working with AI orchestration platforms and frameworks such as Lang Chain, CrewAI, Auto Gen, Strands, or similar technologies.

Familiarity with cloud AI ecosystems including Azure AI Foundry, AWS Bedrock, GCP Gemini AI, M365 Copilot Studio, or comparable platforms.

Experience developing RAG solutions, vector databases, memory systems, prompt engineering strategies, and model optimization techniques.

Knowledge of modern AI development tools, coding assistants, and enterprise AI platforms.

Knowledge of integration protocols, tool-calling frameworks, and API-driven architectures.

Familiarity with responsible AI practices, governance principles, and regulatory frameworks.

Knowledge of LLM infrastructure concepts such as GPU utilization, token economics, and performance optimization.

Strong communication skills with an aptitude for translating technical concepts into business-focused…