Staff Engineer, AI Platform & Architecture (R5449)

Shield AI is seeking a Staff Engineer, AI Platform & Architecture to translate the enterprise AI engineering roadmap into scalable platform architecture, reusable technical patterns, and production-grade shared services. The successful candidate will provide deep technical leadership across AI enablement, responsible AI controls, observability, cost attribution, and reusable component strategy.

Responsibilities

AI Platform Architecture & Standards

• 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.

Reusable Components & Shared Services

• 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.

Responsible AI Engineering & Governance

• 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.

Productivity, Measurement & Technical Leadership

• 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.

Requirements

• 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.