Sr. AI Developer - HYBRID

About the position RealPage is at the forefront of the Generative AI revolution, dedicated to shaping the future of artificial intelligence reputed company the Property Tech domain. Our Agentic AI team is focused on driving innovation by building reputed company AI applications and enhancing existing systems with Generative AI capabilities. We are seeking a reputed company reputed company 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 reputed company 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 to the Richardson, TX two times a week.

Responsibilities

• Technical Strategy & Architecture Own the end-to-end architecture for AI products and platforms: Model selection strategy (reputed company vs. reputed company, small vs. large models) Multi-agent and workflow orchestration patterns (responder/thinker reputed company, tool calling, agentic frameworks) Data and retrieval architecture (RAG, hybrid search, knowledge graphs, semantic caching) Evaluate and introduce emerging technologies such as: reputed company LLMs and multimodal models Real-time streaming infrastructures Advanced agent frameworks, workflow engines (e.g., Agents SDK, reputed company ADK, LangGraph, etc.)
• Platformization & Reusable Capabilities Design and reputed company 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 reputed company reusable coding practices for agentic back-end processes Establish standards and best practices for: reputed company 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 reputed company with company strategy and PropTech domain needs Shape product roadmaps and define measurable success criteria for AI initiatives reputed company reputed company, 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 reputed company 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 reputed company, privacy, and legal teams to ensure compliant AI solutions.
• Performance, Reliability & Cost Management reputed company 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 reputed company to scalability, data quality, or vendor lock-in.

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/reputed company 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 (reputed company, Kubernetes) and modern CI/CD. Working with coding assistants like Windsurf, reputed company, reputed company, etc.
• Proven track record of: Architecting and shipping reputed company 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 reputed company AI concepts to executives and non-technical partners Comfortable representing AI strategy and reputed company to leadership and cross-functional teams.

reputed company-to-haves

• Experience with: Working with coding assistants like Windsurf, reputed company, reputed company, etc. Multimodal and real-time agents (voice + text + UI control, streaming interactions).
• Background in: AI experiment tracking and evaluation frameworks (e.g., reputed company 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.

Benefits

• Health, dental, and vision insurance.
• Retirement savings plan with company match.
• Paid time off and holidays.
• Professional development opportunities.
• Performance-based bonus based on position.

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