Lead AI Engineer – Agentic & Generative AI; DFW Area

Lead AI Engineer – Agentic & Generative AI (DFW Area)

Real Page 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 Lead AI Engineer who is a senior technical leader responsible for driving the strategy, architecture, and delivery of Agentic and Generative AI solutions across our Prop Tech portfolio. You will define and implement the technical roadmap for AI systems, mentor the AI engineering team, and collaborate with executives and product leaders to identify high-impact AI opportunities.

You will design robust, scalable AI platforms that leverage foundation models, RAG, multi-agent systems, and emerging technologies to create differentiated experiences for our customers.

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.

• Technical Strategy & Architecture
  • 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)
  • Evaluate and introduce emerging technologies such as:
    • Next-generation LLMs and multimodal models
    • Real-time streaming infrastructures
    • Advanced agent frameworks, workflow engines (e.g., Agents SDK, Google ADK, Lang Graph, etc.)
  • Platformization & Reusable Capabilities
    • Design and lead the implementation of shared AI services and SDKs:
      • Reusable RAG pipelines and ingestion frameworks
      • Common UI components and design patterns for AI copilots and agents
      • Modular reusable coding practices for agentic back-end processes
    • Establish standards and best practices for:
      • Prompt design and versioning
      • Model and retrieval evaluation
      • Observability, logging, and incident response for AI systems.
    • Leadership & Mentoring
      • Provide hands-on technical leadership to AI Engineers, ML Engineers, and Data Scientists:
        • Guide architectural decisions and code quality
        • Conduct thorough design and code reviews
        • Mentor team members in LLMs, RAG, agentic design, and production AI practices
      • Help define and grow the AI engineering culture, focusing on innovation, quality, and responsible AI.
    • Delivery & Stakeholder Management
      • Partner closely with Product, Design, and Business stakeholders to:
        • Identify high-value AI use cases aligned with company strategy and Prop Tech domain needs
        • Shape product roadmaps and define measurable success criteria for AI initiatives
      • Lead complex, cross-functional AI projects from concept to production, ensuring:
        • Clear requirement definitions and project plans
        • On-time delivery with high quality and reliability
        • Ongoing iteration based on user feedback and metrics.
      • Evaluation, Governance & Responsible AI
        • Define robust evaluation frameworks:
          • Offline and online metrics for relevance, safety, user satisfaction, and business impact
          • Human evaluation workflows for complex or sensitive tasks
        • Drive AI governance and responsible AI practices:
          • Content safety, bias and fairness considerations, PII handling
          • Compliance with internal policies and external regulations (e.g., GDPR‑like requirements, data residency)
        • Collaborate with security, privacy, and legal teams to ensure compliant AI solutions.
      • Performance, Reliability & Cost Management
        • Lead performance and cost optimization for AI systems:
          • Model routing, distillation, and caching strategies
          • Right‑sizing infrastructure and making build‑vs‑buy decisions
          • SLAs/SLOs for key AI services, including latency, uptime, and error budgets.
        • Proactively identify and mitigate technical risks related to scalability, data quality, or vendor lock‑in.
    Qualifications
    • Required Knowledge / Skills / Abilities
      • Typically 8+ years of experience in Software Engineering, ML Engineering, or Data Science, with 3+ years hands‑on in Applied AI/LLMs and at least 2+ years in a senior/lead role.
      • Deep expertise…