AI Engineer / Architect

Internal AI Solution Development
Design and build internal AI solutions that support engineering productivity and software delivery, including:
AI-powered developer workflows and assistants
Agentic SDLC automation patterns
Internal tools for code analysis, documentation, testing, migration, and engineering support
Reusable prompt, tool-calling, and workflow patterns Reference implementations that can be adopted by engineering teams
Develop solutions that are practical, scalable, maintainable, and aligned with RealPage engineering standards. Agentic Workflow and Platform Enablement
Build reusable capabilities that help teams adopt AI consistently across the organization, including:
Multi-step agentic workflows
Tool-calling and orchestration patterns
RAG-based internal knowledge solutions
Shared SDKs, templates, and integration examples
Reusable components for copilots, agents, and AI-enabled engineering workflows
Partner with senior architects and engineering leaders to establish patterns that can scale beyond one team or use case. Engineering Team Enablement
Work directly with engineering teams, champions, and internal stakeholders to help them adopt AI effectively. Responsibilities include:
Pairing with teams on AI use cases and implementation patterns
Providing technical guidance on LLM, RAG, and agentic workflow design
Supporting proof-of-concept efforts and helping mature them into repeatable practices
Creating playbooks, examples, templates, and documentation for internal engineering use
Participating in office hours, workshops, and AI enablement sessions AI Evaluation, Quality, and Responsible Use
Help define and apply practical evaluation and governance practices for internal AI solutions, including:
Prompt and workflow evaluation
Accuracy, relevance, and usefulness testing
Safety and responsible AI considerations
PII and sensitive-data handling
Logging, observability, and feedback loops
Human-in-the-loop review patterns where appropriate
Ensure internal AI solutions are developed with quality, security, privacy, and reliability in mind. Delivery and Cross-Functional Collaboration
Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases. Responsibilities include:
Translating engineering productivity needs into AI-enabled solutions
Supporting roadmap-aligned internal AI initiatives
Contributing to adoption and capacity-improvement goals
Helping measure the impact of AI enablement efforts
Communicating technical concepts clearly to engineering and non-engineering audiences Performance, Reliability, and Cost Awareness
Design AI solutions with practical performance and cost considerations, including:
Model selection and routing
Prompt and context optimization
Caching and retrieval efficiency
Latency and reliability considerations
Build-vs-buy recommendations Avoidance of vendor lock-in where practical

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