AI Developer IV

RealPage, Inc.

. Design and build internal AI solutions that improve engineering productivity and software delivery .

Posted 9/29/2026full-timeRemote • Oregon • United StatesMid-LevelSenior💰 $159,100 - $270,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building AI solutions that enhance engineering productivity, with a strong focus on LLM, RAG architectures, and cloud-native application development. Proficient in creating reusable workflows, documentation, and tools that support AI adoption and internal engineering practices.

Highest-signal resume keywords

AI Solution DesignLLM Application DevelopmentCloud-Native Application DevelopmentPython ProgrammingPrompt Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringAI-Powered WorkflowsAgentic Workflow DesignMulti-Step Workflow OrchestrationTool CallingRAG ArchitecturesVector DatabasesCI/CDAPI DesignAutomated Testing

Soft Skills

Strong Communication SkillsCollaboration with Engineering Teams

Tools & Technologies

AzureGCPAWSGitHub CopilotCursorWindsurfCodexLangGraphOpenAI Agents SDKPlaywright

Industry Keywords

Generative AIInternal Developer PlatformsEngineering Productivity ToolsPrivacy ComplianceEnterprise Software

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaScriptPythonSDLCTypeScript

About the role

Key responsibilities & impact

Design and build internal AI solutions that improve engineering productivity and software delivery
Develop AI-powered developer workflows and assistants, agentic SDLC automation patterns, and internal tools for code analysis, documentation, testing, migration, and engineering support
Create reusable prompt, tool-calling, workflow, and reference implementation patterns
Build multi-step agentic workflows, tool-calling and orchestration patterns, RAG-based internal knowledge solutions, shared SDKs, templates, integration examples, and reusable components for copilots and agents
Partner with senior architects and engineering leaders to establish scalable patterns
Work directly with engineering teams, champions, and internal stakeholders to support AI adoption
Pair with teams on AI use cases and implementation patterns
Provide technical guidance on LLM, RAG, and agentic workflow design
Support proof-of-concept efforts and mature them into repeatable practices
Create playbooks, examples, templates, and documentation for internal engineering use
Participate in office hours, workshops, and AI enablement sessions
Help define and apply evaluation and governance practices for prompt and workflow evaluation, accuracy, relevance, usefulness, safety, responsible AI, PII handling, logging, observability, feedback loops, and human-in-the-loop review
Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases
Translate engineering productivity needs into AI-enabled solutions and support roadmap-aligned internal AI initiatives
Contribute to adoption and capacity-improvement goals and help measure AI enablement impact
Communicate technical concepts clearly to engineering and non-engineering audiences
Design AI solutions with model selection and routing, prompt and context optimization, caching and retrieval efficiency, latency, reliability, build-vs-buy, and vendor lock-in considerations

Requirements

What you’ll need

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 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: Experience building internal developer platforms, engineering productivity tools, or enablement frameworks
Nice-to-have: Experience with agent frameworks or orchestration tools such as LangGraph, OpenAI Agents SDK, Google ADK, Semantic Kernel, CrewAI, or similar frameworks
Nice-to-have: Experience with evaluation frameworks such as OpenAI Evals, LangSmith Evals, RAGAS, or custom evaluation harnesses
Nice-to-have: Experience with browser automation or workflow automation tools such as Playwright
Nice-to-have: Experience with knowledge management, internal documentation systems, or enterprise search
Nice-to-have: Experience working in environments with privacy, compliance, or regulated-data considerations
Nice-to-have: Background in enterprise software, PropTech, fintech, or other complex business domains
Nice-to-have: Experience supporting AI adoption programs, engineering champions, office hours, or internal technical enablement

Benefits

Comp & perks

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
Additional rewards, including annual bonus and sales incentives depending on the applicable plan, role, and individual performance