Staff Engineer, AI Platform – Architecture

This is a fully remote position, open to applicants in United States.

📋 Description

• Define and advance enterprise AI architecture patterns for the integration of large language models (LLMs), retrieval-augmented generation, agentic workflows, prompt orchestration, and workflow automation.

• Develop reference architectures, conduct design reviews, maintain decision records, and provide implementation guidance to ensure consistent AI development across various business units.

• Act as a technical authority on AI platform decisions, encompassing model selection, integration strategies, data boundary enforcement, and lifecycle management.

• Assess emerging AI technologies and propose appropriate adoption strategies that align with security, operational, and enterprise architecture requirements.

• Collaborate with product, platform, and business technology teams to identify shared needs and translate them into reusable engineering patterns.

• Design and create reusable AI components, including connectors, agents, skill templates, prompt libraries, data pipelines, integration adapters, and service APIs.

• Lead the technical design for shared platform services focused on AI observability, logging, usage metering, evaluation, and lifecycle management.

• Set standards for quality, versioning, deprecation, documentation, and contributions for the shared AI component catalog.

• Guide teams in the adoption of shared components, balancing the need for standardization with practical implementation requirements.

• Identify opportunities to reduce redundant AI engineering efforts through consolidation, abstraction, and platformization.

• Design engineering controls for access management, data classification enforcement, prompt safety, output validation, audit logging, and compliance with policies.

• Collaborate with Security, Legal, and compliance stakeholders to integrate responsible AI requirements into development and deployment processes.

• Design governance patterns for model and agent lifecycle management, including version tracking, evaluation, drift monitoring, rollback, and deprecation workflows.

• Create technical dashboards and telemetry to reveal adoption metrics, risks, performance, and compliance with governance across AI-enabled systems.

• Represent engineering perspectives in AI governance reviews and convert policy requirements into actionable technical standards.

• Develop AI-assisted workflow patterns that enhance individual productivity, team collaboration, knowledge retrieval, meeting intelligence, document generation, and task automation.

• Establish measurement methodologies that link AI usage to time savings, quality enhancements, error reduction, capacity creation, and business value.

• Collaborate with Finance and platform teams to create cost metering, showback/chargeback, and optimization mechanisms for AI services.

• Mentor senior and mid-level engineers, enhance engineering quality, and spearhead complex cross-functional technical initiatives from conception to production.

• Contribute to communities of practice, internal enablement resources, and technical advocacy for enterprise AI engineering standards.

⛳️ Requirements

• Progressive experience in enterprise software engineering, AI platform engineering, data platform engineering, or roles related to digital workplace technology.

• Extensive hands-on expertise in generative AI, large language model integration, retrieval-augmented generation architectures, agentic AI patterns, prompt orchestration, and the design of production AI systems.

• Experience in designing shared platform services, reusable component libraries, APIs, integration frameworks, or developer enablement platforms utilized by multiple teams.

• Strong architectural judgment regarding security, reliability, scalability, observability, maintainability, and operational cost trade-offs.

• Experience in implementing or contributing to AI governance controls such as access management, data classification, audit logging, model lifecycle management, and compliance-focused development practices.

• Ability to influence technical direction across matrixed teams through architecture reviews, written guidance, reference implementations, and hands-on collaboration.

• Experience in defining metrics, telemetry, or attribution mechanisms for adoption, productivity, cost, quality, or operational performance.

• Excellent written and verbal communication skills, with the capability to articulate complex AI engineering concepts to both technical and non-technical audiences.

🏝️ Benefits

• Compensation within the listed range + Bonus + Benefits + Equity

• Temporary benefits package (available after 60 days of employment)