Overview
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 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 PropTech 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.
Responsibilities
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, LangGraph, 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 PropTech 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
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 TypeScript/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: Architecting 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 (prom
