Lead AI Engineer

Overview

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.

You will lead 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.

• Technical Strategy & Architecture:
  Own the end-to-end architecture for AI products and platforms, including 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, and advanced agent frameworks/workflow engines (e.g., Agents SDK, Google ADK, Lang Graph).
• Platformization & Reusable Capabilities: design and lead the implementation of shared AI services and SDKs (reusable RAG pipelines, 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 design and code reviews; mentor in LLMs, RAG, agentic design, and production AI practices; help define and grow AI engineering culture focusing on innovation, quality, and responsible AI.
• Delivery & Stakeholder Management: partner with Product, Design, and Business stakeholders to identify high-value AI use cases, shape product roadmaps, and define measurable success criteria for AI initiatives; lead complex cross-functional AI projects from concept to production with clear requirements, on-time delivery, quality, reliability, and ongoing iteration based on user feedback and metrics.
• Evaluation, Governance & Responsible AI: define robust evaluation frameworks (offline/online metrics for relevance, safety, user satisfaction, business impact); implement human evaluation workflows where needed; drive governance practices including content safety, bias and fairness considerations, PII handling, and compliance with internal policies and external regulations; collaborate with security, privacy, and legal teams.
• Performance, Reliability & Cost Management: lead performance and cost optimization for AI systems (model routing, distillation, caching); manage infrastructure scale and build-vs-buy decisions; establish SLAs/SLOs for key AI services (latency, uptime, error budgets); proactively identify and mitigate risks related to scalability, data quality, or vendor lock-in.

• 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 in:
  • Python and Type Script/JavaScript in production environments
  • Designing and operating distributed, cloud-native systems (GCP, Azure, or AWS)
  • Containerization and orchestration (Docker, Kubernetes) and modern CI/CD
  • Working with coding assistants like Windsurf, Cursor, Codex, etc.
• Proven track record of: architecture and shipping complex AI systems to production at scale; leading multi-engineer initiatives and mentoring others; making data-driven tradeoffs between speed, quality, and cost.
• Advanced experience with: LLM-based application design (prompting, tool use, function calling, multi-agent workflows); RAG architectures, vector databases, and retrieval optimization techniques; AI observability, monitoring, and evaluation frameworks.
• Excellent communication and stakeholder management skills: ability to communicate complex AI concepts to executives and non-technical partners; comfortable representing AI strategy and progress to leadership and cross-functional teams.

• Experience with: working with coding assistants like Windsurf, Cursor, Codex, etc.
• Multimodal and real-time agents (voice + text + UI control, streaming interactions).
• Background in: AI experiment tracking and evaluation frameworks (e.g., OpenAI Evals, Langsmith Evals, etc.); data platforms…