Software Engineer - Data & AI

Overview

In this role you will design, build, and operate AI-enabled web applications and interfaces that power McLaren’s data-driven experiences. You’ll work in a product-focused IT team, delivering polished front-ends and the AI integration layer that supports streaming reasoning and data insights. The role blends frontend excellence with backend and cloud work, contributing to scalable, secure AI-powered products. This is a chance to shape how data and AI are experienced by users across the business.

Pay / Benefits

• Structured career development framework
• 25 days’ holiday plus bank holiday
• Enhanced company pension
• Discretionary annual bonus
• Private medical insurance and health cash plan
• Life assurance benefit

Responsibilities

• Design, build, and maintain responsive, accessible web apps and AI-enabled interfaces (chat UIs, dashboards)
• Create AI-enabled UX with transparency: streaming, citations, confidence indicators, human-in-the-loop
• Integrate frontends with McLaren AI platforms, manage streaming, token/rate limits, retries
• Develop backend services and APIs to support AI features and data products
• Deliver AI products from POC/POI to MVP and production with clean data source connections
• Own infrastructure: IaC, CI/CD, containerization, observability, deployment
• Contribute to AI evaluation and quality via automated tests and monitoring
• Apply governance and security by design: access, data lineage, encryption, least privilege
• Promote McLaren-controlled infrastructure and data residency, drive AI tool adoption
• Plan work in sprints, balance R&D with production delivery, provide honest estimates

Key requirements

• Python (FastAPI) and Node.js
• REST APIs, SQL/PostgreSQL
• Redis/Valkey for sessions/WebSocket coordination
• JavaScript/TypeScript, HTML5, CSS, React or equivalent
• Responsive design and web performance (Core Web Vitals)
• Git-based workflows, CI/CD
• Containerisation and observability tools
• Rust for performance-critical components
• Fine-tuning (LoRA/PEFT) and multimodal models
• Familiarity with AWS GenAI-on-EKS pattern (LiteLLM, vector DB, Langfuse, Terraform/Helm)
• Data visualization and Figma literacy

• Agile team collaboration
• Ownership and proactive risk flagging
• Clear communication and stakeholder engagement
• Full-stack web development
• AI/LLM integration and evaluation
• MLOps and AI governance