Lead AI Engineer

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:

Primary Responsibilities

1. Technical Strategy & Architecture

2. Own the end-to-end architecture for AI products and platforms:

3. Model selection strategy (Google vs. OpenAI, small vs. large models)

4. Multi-agent and workflow orchestration patterns (responder/thinker pattern, tool calling, agentic frameworks)

5. Data and retrieval architecture (RAG, hybrid search, knowledge graphs, semantic caching)

6. Evaluate and introduce emerging technologies such as:

7. Next-generation LLMs and multimodal models

8. Real-time streaming infrastructures

9. Advanced agent frameworks, workflow engines (e.g., Agents SDK, Google ADK, LangGraph, etc.)

10. Platformization & Reusable Capabilities

11. Design and lead the implementation of shared AI services and SDKs:

12. Reusable RAG pipelines and ingestion frameworks

13. Common UI components and design patterns for AI copilots and agents

14. Modular reusable coding practices for agentic back-end processes

15. Establish standards and best practices for:

16. Prompt design and versioning

17. Model and retrieval evaluation

18. Observability, logging, and incident response for AI systems.

19. Leadership & Mentoring

20. Provide hands-on technical leadership to AI Engineers, ML Engineers, and Data Scientists:

21. Guide architectural decisions and code quality

22. Conduct thorough design and code reviews

23. Mentor team members in LLMs, RAG, agentic design, and production AI practices

24. Help define and grow the AI engineering culture, focusing on innovation, quality, and responsible AI.

25. Delivery & Stakeholder Management

26. Partner closely with Product, Design, and Business stakeholders to:

27. Identify high-value AI use cases aligned with company strategy and PropTech domain needs

28. Shape product roadmaps and define measurable success criteria for AI initiatives

29. Lead complex, cross-functional AI projects from concept to production, ensuring:

30. Clear requirement definitions and project plans

31. On-time delivery with high quality and reliability

32. Ongoing iteration based on user feedback and metrics.

33. Evaluation, Governance & Responsible AI

34. Define robust evaluation frameworks:

35. Offline and online metrics for relevance, safety, user satisfaction, and business impact

36. Human evaluation workflows for complex or sensitive tasks

37. Drive AI governance and responsible AI practices:

38. Content safety, bias and fairness considerations, PII handling

39. Compliance with internal policies and external regulations (e.g., GDPR-like requirements, data residency)

40. Collaborate with security, privacy, and legal teams to ensure compliant AI solutions.

41. Performance, Reliability & Cost Management

42. Lead performance and cost optimization for AI systems:

43. Model routing, distillation, and caching strategies

44. Right-sizing infrastructure and making build-vs-buy decisions

45. SLAs/SLOs for key AI services, including latency, uptime, and error budgets.

46. 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 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 (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.

Nice-to-Have Skills / Abilities

• 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 (data lakes/warehouses, feature stores, event streams like Kafka)

• Browser automation software such as PlayWright

• Designing AI products in domains with strong regulatory or privacy constraints.

• Experience building organizational AI strategies, setting standards, and helping define AI hiring and capability roadmaps. #LI-JL1 #LI-REMOTE

Pay Range: USD $125, USD $213,900.00 /Yr.