Staff Engineer, AI Platform & Architecture (R5449)

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI ’s technology actively supports operations worldwide. For more information, visit . Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
Job Description:

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.

What you'll do:

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, do