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Lead AI Engineer

RealPage, Inc. -
Richardson, TX

Job Details

$125,700 - $213,900 a year

Qualifications

AI models
Containerization systems
Performance monitoring
Cost management
Software engineering
Scalable systems
Prompt engineering
Infrastructure architecture design
Databases
Data Architecture Design (Architecture design skills)
Production systems
Machine intelligence
JavaScript
Continuous integration
Developing large-scale AI models
Mentoring
Cloud Native Design
Leading team collaboration initiatives
Scalability
Data science
DevOps automation
Cross-functional team management
Model evaluation
TypeScript
Technical architecture
Project stakeholder communication
Python
Cross-functional communication
Senior leadership
Stakeholder management

Full Job Description

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.

We are looking for teammates that are willing to come into the office 2 times a week in Richardson, TX.

Responsibilities:

Primary Responsibilities

1. 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.)

2. 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.

3. 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.

4. 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.

5. 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.

6. 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 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-JK1 #LI-HYBRID

We are looking for teammates that are willing to come into the office 2 times a week in Richardson, TX.

Pay Range: USD $125,700.00 - USD $213,900.00 /Yr.

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