Senior AI Platform Architect

Shield

Oslo

On-site

NOK 1,810,000 - 2,762,000

Full time

14 days+

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Job summary

Shield AI seeks a Staff Engineer, AI Engineering to translate the enterprise AI roadmap into scalable platform architecture and reusable services. You will lead across AI enablement, responsible AI controls, observability, and cost attribution, guiding teams toward secure, measurable AI adoption without direct authority.

You will mentor engineers, set standards, review designs, and champion production-grade AI components used by multiple business units.

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, LLM integration, RAG architectures, agentic AI patterns, 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 AI governance controls such as access management, data classification, audit logging, 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.

Responsibilities

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 productivity, knowledge retrieval, and automation.
Design measurement approaches that connect AI usage to time savings, quality improvement, and cost savings.
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.
Contribute to communities of practice and internal enablement material.