Staff Engineer, AI Platform – Architecture

🕒 July 25

🇺🇸 United States – Remote

💵 $190k - $290k / year

⏰ Full Time

🔴 Lead

🤖 AI Engineer

👻 Ghost score 3%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Shield AI

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2015

🤖 Artificial Intelligence

🚀 Aerospace

🎖️ Defense

Artificial Intelligence • Aerospace • Defense

Shield AI is a leading developer of AI-driven military solutions, focusing on enhancing mission autonomy and battlefield awareness. Their platform, Hivemind, enables rapid deployment of intelligent systems for various defense applications, including drone operation and surveillance. With a commitment to utilizing advanced technology, Shield AI aims to protect service members and civilians by revolutionizing defense technologies through autonomous systems.

📋 Description

• 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.

🎯 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.

🏖️ Benefits

• Pay within range listed + Bonus + Benefits + Equity • Temporary benefits package (applicable after 60 days of employment)