Job Specifications
Job Description
The AI Platform Lead owns the definition, creation, and ongoing management of ULS’s enterprise AI platform and roadmap; prioritizing platform reliability, security, governance, and scale; and ensuring solution teams can safely build, deploy, and operate AI capabilities across the full product lifecycle.
The AI Platform Lead will also collaborate and partner closely with strategic functions across the enterprise including Business Stakeholders, Cyber Security, Enterprise Architecture, Risk & Compliance, and strategic vendors to align platform capabilities to business needs, accelerate adoption, and keep UL at the forefront of responsible, enterprise‑scale AI.
This role is fully remote and may be based in any of the following countries, subject to valid work authorisation: United Kingdom, Ireland, Spain, or Poland.
Responsibilities
The AI Platform Engineering Lead owns the definition, creation, and ongoing management of UL’s enterprise AI platform and roadmap; prioritizing platform reliability, security, governance, and scale; and ensuring solution teams can safely build, deploy, and operate AI capabilities across the full product lifecycle.
The AI Platform Engineering Lead defines and owns standards, reference architectures, guardrails, and quality processes; provides architectural coaching to squads and mentoring to technical contributors; and is expected to set the technical direction of AI capabilities, informing our product strategies, and ensuring we drive enterprise innovation through AI.
The AI Platform Engineering Lead collaborates and partners with strategic functions across the enterprise including Business Stakeholders, Cyber Security, Enterprise Architecture, Risk & Compliance, and strategic vendors to align platform capabilities to business needs, accelerate adoption, and keep UL at the forefront of responsible, enterprise‑scale AI.
Provide architectural leadership for AI/ML systems including agentic workflows, RAG/Graph‑RAG pipelines, knowledge‑graph‑enhanced retrieval, and LLM‑powered applications while proactively staying current with emerging AI paradigms, architectural patterns, and platform technologies and guiding the organization through the shifts.
Ensure the AI platform and tools meet enterprise‑grade expectations for compatibility, scalability, resilience, and global availability.
Enable consistent scaling of AI capabilities across business units by defining consumable platform services, reusable patterns, onboarding pathways, and cross‑team integration practices that reduce duplication and accelerate adoption.
Define the long‑term scaling model for the AI platform, ensuring headroom for growth through capacity planning, performance modelling, and cost‑efficient architectural strategies that anticipate future demand.
Own, publish, and track platform metrics KPIs/SLAs/SLOs (availability, latency, success rates, cost per task/inference).
Drive cross‑organizational alignment on standards around AI development and use by influencing teams to adopt reference architectures, security and Responsible AI guardrails, and consistent release and change‑management practices.
Define and maintain AI platform architecture patterns, including agent orchestration, multi-agent coordination, tool‑use patterns, vector search design, and governance for generative and agentic systems.
Ensure production‑ready delivery by setting the technical standards, architectural guardrails, and quality expectations; maintain oversight of technical quality across infrastructure, software, and AI components, including security, observability, operational readiness, cost management, and platform governance.
Establish and enforce engineering quality and Responsible AI standards: architecture/design reviews, automated testing strategies, GenAI/LLM evaluation, bias/fairness, model governance, and secure SDLC for agentic/RAG/Graph‑RAG components.
Drive operational excellence by guiding the team in implementing platform‑wide observability (metrics, dashboards, alerts) and capacity/cost guardrails while actively instilling an engineering mindset that values reliability, measurement, and continuous operational improvement.
Mentor team members across AI/ML engineering, platform engineering, and full‑stack development, fostering growth in AI system design.
Responsible for issue remediation within the team: define processes and ensure root‑cause analysis, verified remediation, and preventative actions are implemented and tracked to completion across AI services, retrieval pipelines, and platform components.
Define and own a cohesive lifecycle strategy for AI‑ and agentic‑first solutions—continuous evaluation (automated and human‑in‑the‑loop), feedback loops, scheduled/triggered retraining, and where appropriate reinforcement learning from human/AI feedback—so systems remain accurate, safe, and cost‑effective over time.
Qualifications
Bachelor’s degree in Computer Science, Engineering, Machine Learning, or rela