RealPage is accelerating the adoption of Generative AI and agentic engineering practices across its technology organization. The Internal AI Center of Excellence is responsible for enabling engineering teams to apply AI effectively, safely, and consistently across the software development lifecycle. 

We are seeking an AI Developer III to help design, build, and scale internal AI solutions that improve engineering productivity, accelerate delivery, and support RealPage’s AI adoption goals. This role will focus on developing reusable AI patterns, agentic workflows, internal developer tools, reference implementations, and enablement assets that help engineering teams move from experimentation to repeatable production use. 

The ideal candidate is a hands-on AI engineer with strong software development experience, practical knowledge of LLMs and agentic systems, and the ability to partner with engineering teams to turn AI concepts into usable internal capabilities. 

1. 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. 

2. 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. 

3. 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 

4. 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. 

5. 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 

• Typically 6+ years of software engineering experience, with meaningful hands-on experience building production applications or internal platforms. 

• 2+ years of applied AI, LLM, Generative AI, or agentic workflow experience. 

• Strong programming experience in Python, TypeScript/JavaScript, or similar production languages. 

• Experience designing and building cloud-native applications or services in Azure, GCP, or AWS. 

• Practical experience with:  

LLM-based application development 

Prompt engineering and prompt versioning 

Tool calling / function calling 

RAG architectures 

Vector databases or semantic retrieval 

Multi-step workflow or agent orchestration 

• Familiarity with modern software engineering practices, including:  

• CI/CD 

• Git-based development 

• Automated testing 

• API design 

• Observability and logging 

• Experience using or enabling AI coding tools such as GitHub Copilot, Cursor, Windsurf, Codex, or similar tools. 

• Ability to work directly with engineering teams to understand needs, prototype solutions, and drive adoption. 
• Strong communication skills with the ability to explain AI concepts and implementation patterns clearly. 

Nice-to-Have Skills / Abilities 

• Experience building internal developer platforms, engineering productivity tools, or enablement frameworks. 

• Experience with agent frameworks or orchestration tools such as LangGraph, OpenAI Agents SDK, Google ADK, Semantic Kernel, CrewAI, or similar frameworks. 

• Experience with evaluation frameworks such as OpenAI Evals, LangSmith Evals, RAGAS, or custom evaluation harnesses. 

• Experience with browser automation or workflow automation tools such as Playwright. 

• Experience with knowledge management, internal documentation systems, or enterprise search. 

• Experience working in environments with privacy, compliance, or regulated-data considerations. 

• Background in enterprise software, PropTech, fintech, or other complex business domains. 

• Experience supporting AI adoption programs, engineering champions, office hours, or internal technical enablement. 

• Health, dental, and vision insurance.
• Retirement savings plan with company match.
• Paid time off and holidays.
• Professional development opportunities.
• Performance-based bonus based on position.   

Compensation may vary depending on your location, qualifications including job-related education, training, experience, licensure, and certification, that could result at a level outside of these ranges. Certain roles are eligible for additional rewards, including annual bonus, and sales incentives depending on the terms of the applicable plan and role as well as individual performance.