Staff AI Platform & Architecture Engineer, Shield AI

• Progressive experience in enterprise software engineering, AI platform engineering, data platform engineering, or digital workplace technology roles
• Deep hands-on understanding of generative AI, large language model integration, RAG architectures, agentic AI patterns, prompt orchestration, and production AI system design
• Experience designing shared platform services, reusable component libraries, APIs, integration frameworks, or developer enablement platforms used by multiple teams
• Strong architecture judgment across security, reliability, scalability, observability, maintainability, and operational cost tradeoffs
• Experience implementing or contributing to AI governance controls such as access management, data classification, audit logging, model lifecycle management, and compliance-aware development practices
• Ability to influence technical direction across matrixed teams through architecture reviews, written guidance, reference implementations, and hands-on collaboration
• Experience defining metrics, telemetry, or attribution mechanisms for adoption, productivity, cost, quality, or operational performance
• Strong written and verbal communication skills with the ability to explain complex AI engineering concepts to technical and non-technical audiences

Desirable

• The Staff Engineer, AI Engineering is a senior individual contributor responsible for translating the enterprise AI engineering roadmap into scalable platform architecture, reusable technical patterns, and production-grade shared services
• Reporting to the Director, AI Engineering, this role provides deep technical leadership across AI enablement, responsible AI controls, observability, cost attribution, and reusable component strategy
• The Staff Engineer acts as the connective technical tissue across Engineering, IT, Security, Legal, Data, and business unit teams - setting standards, creating reference implementations, and guiding teams toward consistent, secure, measurable AI adoption without relying on direct authority
• Success is defined by high-quality platform components adopted across teams, clear architecture and governance patterns, measurable productivity and cost outcomes, and effective mentorship of engineers building AI-enabled capabilities
• Define and evolve enterprise AI architecture patterns for LLM integration, retrieval-augmented generation, agentic workflows, prompt orchestration, and workflow automation
• Create reference architectures, design reviews, decision records, and implementation guidance that enable consistent AI development across business units
• Serve as a technical authority for AI platform decisions, including model selection, integration approaches, data boundary enforcement, and lifecycle management
• Evaluate emerging AI technologies and recommend fit-for-purpose adoption paths aligned to security, operational, and enterprise architecture requirements
• Partner with product, platform, and business technology teams to identify common needs and convert them into reusable engineering patterns
• Design and build reusable AI components such as connectors, agents, skill templates, prompt libraries, data pipelines, integration adapters, and service APIs
• Lead technical design for shared platform services for AI observability, logging, usage metering, evaluation, and lifecycle management
• Establish quality, versioning, deprecation, documentation, and contribution standards for the shared AI component catalog
• Guide teams through adoption of shared components, balancing standardization with practical implementation needs
• Identify opportunities to eliminate duplicate AI engineering efforts through consolidation, abstractions, and platformization
• Architect engineering controls for access management, data classification enforcement, prompt safety, output validation, audit logging, and policy adherence
• Partner with Security, Legal, and compliance stakeholders to embed responsible AI requirements into development and deployment pipelines
• Design model and agent lifecycle governance patterns, including version tracking, evaluation, drift monitoring, rollback, and deprecation workflows
• Build technical dashboards and telemetry that expose adoption, risk, performance, and governance compliance across AI-enabled systems
• Represent engineering considerations in AI governance reviews and translate policy requirements into implementable technical standards
• Develop AI-assisted workflow patterns that improve individual productivity, team collaboration, knowledge retrieval, meeting intelligence, document generation, and task automation
• Design measurement approaches that connect AI usage to time savings, quality improvement, error reduction, capacity creation, and business value
• Partner with Finance and platform teams to develop cost metering, showback/chargeback, and optimization mechanisms for AI services
• Mentor senior and mid-level engineers, raise engineering quality, and lead complex cross-functional technical initiatives from concept through production
• Contribute to communities of practice, internal enablement material, and technical evangelism for enterprise AI engineering standards