Staff Engineer, AI Platform – Architecture

Shield AI

Staff Engineer responsible for guiding AI platform architecture and reusable component strategy at Shield AI. Leading engineering efforts for AI enablement and governance.

Posted 7/25/2026full-timeRemote • 🇺🇸 United StatesLead💰 $190,000 - $290,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in enterprise AI architecture, focusing on LLM integration, prompt orchestration, and workflow automation. Capable of leading cross-functional initiatives, establishing governance standards, and mentoring engineering teams to enhance AI development practices.

Highest-signal resume keywords

Generative AILarge Language Model IntegrationAI Governance ControlsShared Platform Services DesignArchitecture Judgment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Architecture PatternsRAG ArchitecturesPrompt OrchestrationReusable Component LibrariesAPIsIntegration FrameworksModel Lifecycle ManagementTelemetryMetrics DefinitionData Classification

Soft Skills

Strong Written CommunicationStrong Verbal CommunicationInfluencing Technical Direction

Tools & Technologies

AI PlatformsIntegration AdaptersService APIsTechnical Dashboards

Industry Keywords

Enterprise Software EngineeringDigital Workplace TechnologyCompliance-Aware DevelopmentOperational Cost Tradeoffs

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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