Developer IV

RealPage, Inc.•Richardson, TX

2d•$125,700 - $213,900

About The Position

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.

Requirements

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, and multi-step workflow or agent orchestration.
Familiarity with modern software engineering practices, including CI/CD, Git-based development, automated testing, API design, and 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 Haves

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.

Responsibilities

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, and reference implementations that can be adopted by engineering teams.
Develop solutions that are practical, scalable, maintainable, and aligned with RealPage engineering standards.
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, and 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.
Work directly with engineering teams, champions, and internal stakeholders to help them adopt AI effectively.
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.
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, and human-in-the-loop review patterns where appropriate.
Ensure internal AI solutions are developed with quality, security, privacy, and reliability in mind.
Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases.
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.
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, and avoidance of vendor lock-in where practical.