Principal AI Engineer in London

Who are We? Grip is the AI-powered, end-to-end event platform built for engagement. The flexible system helps commercial event organisers boost revenue by establishing, maintaining and tracking relationships between participants over multiple events. Grip goes beyond networking — combining AI with billions of interactions happening across the platform with a powerful event management system, seamless registration and award-winning mobile event app so participants meet the right people at the right time.

The Opportunity Every interaction on Grip's platform generates data with real commercial value to organisers, exhibitors and attendees. We're at an inflection point where AI makes it possible to turn that raw activity into genuinely intelligent products: better recommendations, sharper insights, and autonomous agents that act on data rather than just report it.

Your Role Own the architecture of Grip's platform-wide data and AI infrastructure — from how event data is captured and modelled, to how it's stored, governed and served to downstream consumers including search, recommendations, analytics, and AI agents. You'll set the standards that squads build against, and you'll directly build the shared infrastructure that no single squad owns. This role reports into the Engineering Director.

• Own platform-wide data architecture: capture standards, data modelling, storage strategy, and serving layers used across the organisation
• Design and build the data infrastructure powering analytics, search, recommendations, and AI/agentic features
• Build and scale data pipelines handling high-volume event interaction data across the platform — both batch and streaming — with reliability and low latency at the core
• Own the data layer that underpins Grip's Agentic products, ensuring agents and MCP-based tools have access to clean, well-modelled, real-time data
• Define data contracts and standards that product squads build against, working as a technical peer to their Principal / Senior Engineers.
• Establish data quality, governance and lineage standards across the organisation, including PII handling in line with GDPR
• Drive the technical strategy for how Grip captures and governs new categories of data in a privacy-compliant, lawful-basis-aware way
• Build observability into data systems — structured logging, freshness and quality monitoring, SLOs, and pipeline health dashboards
• Champion high-quality technical communication: proposals, specifications, and documentation that other teams can build on
• Mentor engineers across the organisation on data architecture and AI infrastructure best practices

Platform Remit Own the shared data layer that sits beneath Engage, Manage and Agentic systems. Expose well-structured data through MCP servers and tools for the Organiser and Exhibitor Agents. Set and maintain shared data contracts, schemas and pipelines consumed across squads. Act as the technical authority on data architecture, search/retrieval design, and AI infrastructure decisions platform-wide. Influence the organisation's approach to data storage, retention and compliance. Partner with squad Principal Engineers as peers when their product work touches shared data infrastructure.

What We Offer Remote-first culture with an office in London Bridge (optional). Competitive salary, equity, and benefits. 25 holiday days per year, sabbatical leave opportunities. Company training and professional development budget. Group life insurance and company health plan. Real influence over technical direction platform-wide.

Career Path & Development We are highly invested in your growth. Your career development is a standing agenda item in regular check-ins with your manager. As a Principal Engineer at Grip, you'll have real influence over technical direction platform-wide, opportunities to mentor across squads, and a clear progression framework. We use quarterly performance reviews to reflect on progress and set ambitious goals aligned with your aspirations.

Our Process Screening call. Product/engineering case study — a problem statement and requirements, working together to understand how you'd approach implementation. Technical assessment — system design and technical interview focused on data architecture, pipelines, and platform trade-offs. Final round with our CEO, Tim Groot, Founder & CEO — culture fit and alignment on vision.

Required Experience & Skills Proven track record owning data architecture at scale in a production SaaS environment, ideally in a platform-level capacity. Strong experience with both operational databases and analytical/data warehouse systems. Experience building and scaling data pipelines using tools such as Kinesis, SQS/SNS, and Lambda. Strong understanding of search and retrieval systems and how data modelling choices affect downstream relevance and ranking. Deep hands-on experience building with LLMs, coding assistants, and agentic systems. Experience with Model Context Protocol (MCP), AI orchestration, or similar agentic frameworks. AI safety fluency — prompt injection, jailbreaks, output validation, guardrail design. Experience with caching and performance at scale. Strong fullstack literacy across TypeScript/Node.js. DevOps fluency: AWS, Kubernetes/EKS, Terraform, CI/CD pipelines. Excellent observability practices. Track record of driving data quality, governance and compliance standards. Exceptional communication and influencing skills. Product mindset — able to translate ambiguous business goals into a concrete, platform-wide technical roadmap.

Nice to Have Experience with event technology, SaaS platforms, or B2B applications. Experience building data infrastructure that directly feeds ML models or AI agent systems. Familiarity with prompt engineering, fine-tuning, or AI model optimisation. Real-time systems, WebSockets, or live data pipelines. Experience with real-time location/proximity data. Prior experience in a platform/infrastructure role distinct from product engineering. Open-source contributions or public portfolio demonstrating data engineering or AI/agentic work. Experience with security compliance frameworks for AI systems.