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 www.shield.ai . 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, 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. Required qualifications: 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. Preferred qualifications: Experience in regulated, defense-adjacent, security-sensitive, or data-governed environments. Background in MLOps, AI observability, model evaluation frameworks, agent evaluation, and production monitoring. Familiarity with enterprise AI tooling ecosystems including copilot platforms, workflow automation suites, vector databases, and enterprise search/RAG platforms. Hands-on experience with enterprise data platforms such as Databricks, Snowflake, lakehouse architectures, or comparable data foundations. Experience implementing usage metering, cost allocation, showback/chargeback, or AI spend optimization capabilities. Track record of mentoring engineers and raising technical standards without relying on direct management authority. Advanced degree in Computer Science, Engineering, Data Science, or a related technical field. #LI-KE1 #LD Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.