RealPage is at the forefront of the Generative AI revolution, dedicated to shaping the future of artificial intelligence within the Property Tech domain. Our Agentic AI team is focused on driving innovation by building next generation AI applications and enhancing existing systems with Generative AI capabilities.

We are seeking a Sr. AI Architect who is a senior technical leader who provides end-to-end technical direction for our Agentic & Generative AI ecosystem across the RealPage platform. You will define the reference architectures, patterns, and guardrails that enable teams to safely and efficiently build AI-powered products in the PropTech domain.

You will work at the intersection of AI, data, and platform engineering—shaping how we use LLMs, RAG, agentic frameworks, and emerging multimodal capabilities at scale. You’ll partner with product and engineering leadership to translate business strategy into an AI architecture roadmap, enabling multiple delivery teams to build on a coherent, secure, and observable AI platform.

This team has been coming to our HQ one week out of the month - it will be expected that you will travel to the corporate HQ from time to time.  

1. Enterprise AI Architecture & Strategy

• Define the overarching AI architecture for RealPage:
• Standard patterns for RAG, multi-agent systems, and workflow orchestration
• Integration with existing microservices, data platforms, and event-driven systems.
• Create and own AI reference architectures, blueprints, and design patterns that individual product teams can adopt and extend.
• Evaluate and recommend vendors and technologies across:
• Model providers (OpenAI, Anthropic, Google, xAI, Meta, Mistral, etc.)
• Vector databases (pgvector, Pinecone, Weaviate, Qdrant , etc.)
• Agent frameworks and orchestration tools (Agents SDK, Google ADK, LangChain, workflow engines like n8n, Zapier, etc.)

2. Platformization & Shared Capabilities

• Own the end-to-end architecture for AI products and platforms:
• Model selection strategy (Google vs. OpenAI, small vs. large models)
• Multi-agent and workflow orchestration patterns (responder/thinker pattern, tool calling, agentic frameworks)
• Data and retrieval architecture (RAG, hybrid search, knowledge graphs, semantic caching)
• Define and champion approaches for:
• Semantic caching, cost optimization, and latency reduction
• Multi-tenant, domain-isolated AI use across RealPage products.

3. Data, Retrieval & Knowledge Architecture

• Partner with Data/Analytics teams to align AI architecture with data strategy:
• Data sourcing from warehouses/lakes, operational databases, event streams.
• Governance for which data can be used in training, retrieval, and inference.
• Architect robust RAG and knowledge systems:
• Taxonomies and metadata standards
• Implement knowledge retrieval process that draws from multiple sources and uses reranking to improve the response quality. 
• Long-context models, memory systems, and potential use of knowledge graphs / vector-native databases.
• Define ingestion and refresh strategies to keep AI knowledge current and trustworthy.

4. Security, Compliance & Responsible AI

• Define architectural guardrails for:
• Data privacy, PII redaction, and tenant isolation
• Encryption, key management, and secure connectivity to model providers
• On-prem / VPC deployments for sensitive workloads where required.
• Collaborate with Security, Privacy, and Legal teams to define policies and patterns for:
• Content safety, toxicity filtering, and jailbreak resistance
• Auditability and traceability of AI decisions (logs, traces, model versions, prompts, and responses).
• Establish a Responsible AI framework in partnership with leadership:
• Guidelines for fairness, bias mitigation, and explainability where appropriate
• Review processes for high-risk AI features.

5. Observability, Evaluation & MLOps

• Architect end-to-end observability for AI systems:
• Traces and spans for prompts, model calls, tool calls, and RAG steps (e.g., OpenTelemetry, LangSmith).
• Metrics for latency, cost, error rates, and model-specific KPIs.
• Define standard evaluation approaches:
• Offline evaluation harnesses for RAG and LLM tasks (e.g., Ragas, TruLens, custom eval suites).
• Online experimentation patterns (A/B tests, feature flags, canary releases).
• Work with MLOps/Platform Engineering to:
• Integrate AI workflows into CI/CD and deployment pipelines

6. Technical Leadership & Cross-Functional Collaboration

• Provide architectural leadership to AI Engineers, ML Engineers, and product teams:
• Support solution design for critical AI features and platforms.
• Conduct design reviews and guide trade-offs (build vs. buy, Google ADK vs. OpenAI SDK).
• Partner with Product leaders to:
• Identify high-leverage AI opportunities based on our data and capabilities
• Translate business strategies into a prioritized, feasible AI roadmap.
• Act as a key technical representative for AI architecture in discussions with senior leadership and external partners.

• Typically 10+ years experience in Software Engineering / ML Engineering / Data Platforms, with 3+ years in applied AI/ML and 2–3+ years in an architect/principal or equivalent role.
• Working with coding assistants like Windsurf, Cursor, Codex, etc.
• Deep experience designing and operating cloud-native systems on AWS, GCP, or Azure, including:
  • Kubernetes, Docker, service meshes, API gateways
  • CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation, etc.).
• Strong expertise in:
  • Python and TypeScript/JavaScript ecosystems
  • Modern architectural styles (microservices, event-driven, serverless, domain-driven design).
• Proven track record architecting and delivering production AI/LLM systems:
  • LLM integration (APIs and/or self-hosting), tool calling, and multi-step/agentic workflows
  • RAG architectures, vector databases, and retrieval optimization at scale
  • Robust observability and evaluation practices for AI-driven products.
• Strong understanding of:
  • Data architecture (data lakes/warehouses, streaming, batch pipelines)
  • Security and compliance considerations for AI (identity, access control, tenant isolation, PII/PHI).
• Excellent communication and leadership:
  • Comfortable engaging with executives, product leaders, and engineering teams
  • Able to create clear architectural documentation, roadmaps, and decision records.

Nice-to-Have Skills / Abilities

• Experience with:
• Multimodal and real-time agents (voice, vision, document understanding, OCR, streaming interactions).
• Background in:
• Building internal platforms or Centers of Excellence for AI
• Establishing architectural governance (ADR processes, reference implementations, tech standards).
• AI in regulated or data-sensitive environments (finance, healthcare, housing, etc.).
• Familiarity with:
• AI product management concepts (user research, experimentation, metrics for AI features)
• Emerging AI stack components (guardrail frameworks, AI-native workflow engines, semantic routers).
• Browser automation software such as PlayWright.

This team has been coming to our HQ one week out of the month - it will be expected that you will travel to the corporate HQ from time to time.  

SALARY AND BENEFITS

• RealPage provides a competitive salary package along with a comprehensive benefit plan that includes:
• 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.

Equal Opportunity Employer: RealPage Company is an equal opportunity employer and committed to creating an inclusive environment for all employees.