• Own the architecture of Workiva’s AI platform and core AI services • Shape how machine learning, Generative AI, and agentic systems are integrated across products • Lead the move from early adoption to production-grade, enterprise-ready systems • Define standards for model serving, retrieval, evaluation, governance, and platform reliability • Lead the design of enterprise agentic systems, including orchestration, workflow execution, memory, and multi-agent coordination • Design and evolve Retrieval-Augmented Generation capabilities for enterprise content and knowledge workflows • Establish evaluation methods and quality frameworks for Generative AI applications • Assess emerging AI technologies and guide adoption strategy for Workiva’s platform • Influence technical direction across teams, products, and platform domains • Mentor Staff and Senior Engineers and help raise the technical bar across the organization • Partner closely with Product, Security, Infrastructure, and Architecture leaders • Align teams around a shared vision for scalable, secure AI at Workiva • Lead secure AI platform design, including authorization, runtime isolation, governance, auditability, and compliance • Establish best practices for AI safety, model governance, and customer data protection • Ensure AI systems meet enterprise expectations for availability, resiliency, observability, and operational support • Design for fault tolerance and operational excellence in regulated, security-conscious environments

• Evolve and maintain our Kubeflow, Feast and Spark-on-Kubernetes infrastructure, ensuring it can handle the increasing complexity of both traditional ML and the new wave of AI. • Design the internal tools, APIs, and abstractions that empower distributed teams to own their entire lifecycle—transitioning our data culture from "centralised bottleneck" to "self-service excellence." • Collaborate with embedded Data Science teams to adapt software engineering best practices (CI/CD, versioning, testing) to ML-specific workflows, raising the bar for production-grade AI across the company. • Drive MLOps best practices and define the specific lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to robust, production-grade solutions by providing top-notch deployment frameworks and automation. • Partner closely with our Infrastructure and Data squads to break silos and integrate ML artifacts into our global Data Catalog, Privacy, and Governance frameworks. • Inspire and empower others by genuinely caring for your own wellbeing and your colleagues.

• Design, develop, and maintain scalable data pipelines to support ingestion, transformation, and delivery into centralized feature stores, model-training workflows, and real-time inference services. • Build and optimize workflows for extracting, storing, and retrieving semantic representations of unstructured data to enable advanced search and retrieval patterns. • Architect and implement lightweight analytics and dashboarding solutions that deliver natural language query experience and AI-backed insights. • Define and execute processes for managing prompt engineering techniques, orchestration flows, and model fine-tuning routines to power conversational interfaces. • Oversee vector data stores and develop efficient indexing methodologies to support retrieval-augmented generation (RAG) workflows. • Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions. • Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines. • Should be willing to stay informed and learn emerging methodologies in data engineering, MLOps and LLM operations.

• Debug and troubleshoot complex deployment and integration challenges across customer environments • Work directly with customer engineering and platform teams to deploy and operationalize AI systems • Design and implement integrations across enterprise AI workflows, APIs, infrastructure, and governance platforms • Deploy and support AI evaluation frameworks, observability solutions, and guardrail implementations • Translate customer deployment challenges into actionable feedback for product and engineering teams • Partner with engineering, infrastructure, security, risk, compliance, and operations stakeholders to navigate enterprise governance requirements • Drive successful customer adoption of AI infrastructure solutions while balancing technical and business requirements