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:
- 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.
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.
Skills Required
- 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 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 to explain complex AI engineering concepts to technical and non-technical audiences
- 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 direct management authority
- Advanced degree in Computer Science, Engineering, Data Science, or a related technical field
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Company materials describe excellent medical, dental, and vision coverage alongside a mental‑health EAP. Site perks such as an onsite gym in DC and a gym discount in San Diego support a health‑focused offering.
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Retirement Support — Careers materials highlight a 401(k) with company match as part of the standard package. A Total Rewards overview emphasizes retirement features within a broader, transparent compensation view.
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Career-Linked Recognition & Rewards — Compensation for in‑demand technical and senior go‑to‑market roles is described as competitive, with visible engineering ranges and top‑end packages. This points to meaningful upside tied to role, level, and scarce skills.
Shield AI Insights
What We Do
At Shield AI, you won't wait years to see your work reach the field. You'll build hardware and software that operates in the real world right now, in the hands of the people who depend on it. Hivemind, our AI pilot, has been flying since 2018. It has flown more than 30 platforms, including an F-16, and it now sits under a U.S. Air Force production contract for Collaborative Combat Aircraft. When you write code or shape a system here, you contribute to technology with a proven flight record and a clear production future. V-BAT flies intelligence, surveillance, and reconnaissance missions with an operational record that stretches from Ukraine to the Indo-Pacific. It delivers eyes where they matter most, in the most demanding conditions on earth. The teams behind it watch their work get tested where the stakes are real. X-BAT takes its first flight this year. It's an AI-piloted fighter that needs no runway, built to operate where traditional aircraft can't. Join now and you help shape a program at its earliest, most formative stage. That's the kind of ground-floor work that defines a career. Do the most impactful work of your life, on problems that matter. Autonomy at this level asks a lot of you. You'll take on problems in perception, planning, and control that few teams anywhere are equipped to solve. You'll work across disciplines, from aerospace and robotics to machine learning and systems engineering, alongside people who hold themselves to an exacting standard and expect the same from you. Our mission is clear: protect service members and civilians with intelligent systems. That purpose runs through every decision, every design review, and every deployment. It's why the work here carries a weight you can feel. Ready to join our mission? Explore our open roles and find where you fit.
Why Work With Us
Founded in 2015 by a former Navy SEAL, Shield AI builds AI pilots and uncrewed aircraft. Veterans aren't an afterthought here, they're at every level. It's why the work carries weight: AI pilots and uncrewed aircraft flying real missions, from Ukraine to the Indo-Pacific, protecting service members and civilians.
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Shield AI Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
