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

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.

Performance, Reliability & Cost Management:
Lead performance and cost optimization for AI systems, including: 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. Remote work permitted.

• The position requires a Bachelor’s degree in Computer Science, Machine Learning, Data Science, or a related field and seven (7) years of experience in the job offered or as a Data Scientist, Software Engineer, ML Engineer, or an acceptable alternate occupation.
• The position requires two (2) years of experience in a senior or leadership role.
• The position requires knowledge and experience with the following skills and technologies:
  • Python and TypeScript or JavaScript in production environments;
  • Designing and operating distributed, cloud-native systems, such as Google Cloud Platform (GCP), Azure, or Amazon Web Services(AWS); Containerization and orchestration, such as Dockers or Kubernetes, and modern Continuous Integration /Continuous Delivery (CI/CD);
  • Working with coding assistants, such as Windsurf, Cursor, Codex, etc.;
  • 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, such as 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;
  • Ability to communicate complex AI concepts to executives and non-technical partners;
  • Experience presenting AI strategy and progress to leadership and cross-functional teams;
  • Advanced experience in context engineering, including prompt design, RAG pipelines, grounding strategies, and multi turn orchestration to optimize LLM reasoning;
  • Ability to build and maintain evaluation harnesses for LLMs, including dataset creation, automated benchmarking, regression testing, and quality of output analysis;
  • Hands on experience with LLM fine tuning , such as SFT, PEFT/LoRA, distillation;
  • Ability to prototype and deliver AI POCs, translating ambiguous business problems into validated technical solutions through rapid experimentation;
  • Ability to read, interpret, andapply scientific research, extracting actionable insights from AI/ML papers and implementing state of the art techniques.

Remote work permitted.

Travel required to Richardson, TX office 1 wk / mo.