Lead AI Engineer HYBRID

Primary Responsibilities

1. Technical Strategy & Architecture

• Own the end-to-end architecture for AI products and platforms:

• Model selectionstrategy ( small vs. large models)

• Multi-agent and workflow orchestration patterns (responder/thinker patterntool callingagenticframeworks)

• 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 ( SDK GoogleADKLangGraphetc.)

& 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 reusablecoding practicesfor 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:

• Guidearchitectural decisions and code quality

• Conduct thorough design and code reviews

• Mentor team members in LLMs RAGagenticdesign 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:

• Identifyhigh-value AI use cases aligned with company strategy andPropTechdomain 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 safetybiasand 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.

• Proactivelyidentifyand mitigate technical risks related to scalability data quality or vendor lock-in.

Required Knowledge / Skills / Abilities

• Typically8 yearsof experience in Software Engineering ML Engineering or Data Science with3 yearshands-on in Applied AI/LLMs and at least2 yearsin a senior/lead role.

• Deepexpertisein:

• Python and TypeScript/JavaScript in production environments

• Designing andoperatingdistributedcloud-native systems (GCP Azure or AWS)

• Containerization and orchestration (Docker Kubernetes) and modern CI/CD.

• Working with coding assistants like Windsurf Cursor Codex etc.

• Proventrack recordof:

• 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 EvalsLangsmithEvals etc.)

• Data platforms (data lakes/warehouses feature stores event streams like Kafka)

• Browser automation software such asPlayWright

• 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.