Senior Data Scientist

Overview

Location: 2201 Lakeside Blvd., Richardson, TX 75082

Position Title: Lead AI Engineer

Hours: Monday – Friday, 8:00 am to 5:00 pm, 40 Hours/Week

Responsibilities

Technical Strategy & Architecture: Own the end-to-end architecture for AI products and platforms, including: Model selection strategy (e.g. Google vs. OpenAI, small vs. large models), multi-agent and workflow orchestration patterns (responder/thinker pattern, tool calling, agentic frameworks), and 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, and 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, including reusable RAG pipelines and ingestion frameworks, common UI components and design patterns for AI copilots and agents, and modular reusable coding practices for agentic back-end processes. Establish standards and best practices for: prompt design and versioning, model and retrieval evaluation, and 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 and 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, and ongoing iteration based on user feedback and metrics.

Evaluation, Governance & Responsible AI: Define robust evaluation frameworks, including 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, including: 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.