Developer IV

Overview 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 IV  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.  Responsibilities 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  Qualifications 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 r