AI Engineer III

RealPage, Inc.•Richardson, TX

4d•Hybrid

About The Position

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.

Requirements

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.
Deep expertise in designing and operating distributed, cloud-native systems (GCP, Azure, or AWS).
Deep expertise in 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.
Proven track record of leading multi-engineer initiatives and mentoring others.
Proven track record of making data-driven tradeoffs between speed, quality, and cost.
Advanced experience with LLM-based application design (prompting, tool use, function calling, multi-agent workflows).
Advanced experience with RAG architectures, vector databases, and retrieval optimization techniques.
Advanced experience with 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 Haves

Experience with working with coding assistants like Windsurf, Cursor, Codex, etc.
Experience with 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.).
Background in data platforms (data lakes/warehouses, feature stores, event streams like Kafka).
Background in browser automation software such as PlayWright.
Experience 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.

Responsibilities

Own the end-to-end architecture for AI products and platforms, including model selection strategy, multi-agent and workflow orchestration patterns, and data and retrieval architecture.
Evaluate and introduce emerging technologies such as next-generation LLMs, multimodal models, real-time streaming infrastructures, and advanced agent frameworks.
Design and lead the implementation of shared AI services and SDKs, including reusable RAG pipelines, common UI components for AI copilots, and modular reusable coding practices.
Establish standards and best practices for prompt design, model and retrieval evaluation, and observability, logging, and incident response for AI systems.
Provide hands-on technical leadership to AI Engineers, ML Engineers, and Data Scientists, guiding architectural decisions, conducting code reviews, and mentoring team members.
Partner closely with Product, Design, and Business stakeholders to identify high-value AI use cases, shape product roadmaps, and lead complex AI projects from concept to production.
Define robust evaluation frameworks for relevance, safety, user satisfaction, and business impact, including offline, online, and human evaluation workflows.
Drive AI governance and responsible AI practices, considering content safety, bias, fairness, PII handling, and compliance with internal policies and external regulations.
Lead performance and cost optimization for AI systems, including model routing, distillation, caching strategies, and infrastructure right-sizing.
Proactively identify and mitigate technical risks related to scalability, data quality, or vendor lock-in.