Role Description We are seeking a Sr MLOps Engineer to build the model lifecycle, deployment, observability, and infrastructure foundations used by multiple production AI features, including recognition, AI Video Search, Multimodal AI, Agentic AI, and Energy AI ship faster with shared, reliable platform primitives. In this role, you will be responsible for: - Build model registry, model serving, deployment, rollback, and CI/CD systems for production AI services. - Own feature, dataset, model, and prompt versioning patterns across AI products. - Standardize training, evaluation, release, monitoring, and operational workflows for AI teams. - Improve reliability, cost efficiency, latency, and repeatability of AI launches. - Create reusable platform patterns across AI features. - Partner with engineering, data science, product, and operations teams to productionize AI capabilities at scale. Qualifications - Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 5+ years of professional experience in software development, applied science, or ML engineering; or - Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 2+ years of professional experience in software development, applied science, or ML engineering. - Experience building production ML platforms, model serving systems, or MLOps workflows. - Strong Python and cloud engineering skills. - Experience with CI/CD, Git, infrastructure-as-code, and production monitoring. - Familiarity with model registry, feature/data versioning (DVC), validation, deployment, rollback, and observability. - Ability to communicate tradeoffs clearly across engineering, data science, and product teams. Requirements - Experience with GCP/AWS, Cloud Run, Kubernetes, Vertex AI, SageMaker, MLflow, or equivalent tools. - Experience with AI services for computer vision, LLMs, multimodal models, or recommendation systems. - Experience with data validation, dataset versioning, feature stores, or model quality monitoring. - Experience optimizing cost, latency, reliability, and operational readiness for AI systems. - Experience with IoT, edge AI, smart home, or distributed device environments. Benefits - Paid holidays and flexible paid time away. - Employee/Friends/Family Discounts. - Medical/dental/vision/life coverage & 24/7 Medical Hotline. - 401(k) + Employer Match. - Employee Resource Groups.

• 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

• Design, train, evaluate, and ship ML systems that power governance and security capabilities, starting with problems like prompt injection detection, behavioral anomaly detection, trust scoring, and policy recommendations. • Build the supporting infrastructure: data pipelines, feature stores, model serving, evaluation harnesses, and the feedback loops that make iteration fast. • Make pragmatic build-vs-buy calls. Use frontier models, off-the-shelf tooling, and managed services to move quickly; invest in custom systems where they create durable advantage. • Set technical direction for the team's ML work. Own the architecture, evaluation methodology, model lifecycle, and the bar for shipping. • Help recruit, mentor, and shape the team as it grows. • This role may require participation in a 24/7 on-call rotation for the Agentic Platform; carry genuine pager responsibility for the services you build and operate.

• Build and deploy ML models to identify missing data, predict treatment delays, triage high-risk referrals, and score payer/patient friction • Architect and maintain LLM-powered workflows • Build and maintain data pipelines to collect, clean, label, and store data across systems • Define success metrics, monitor model performance over time, and iterate quickly to improve accuracy and reliability. Set up training data, data labeling workflows or feedback loops for model improvement. • Collaborate with product, engineering, and operations to identify high-leverage automation opportunities and embed models into workflows.