Founding AI Software Engineer | Global Media | London / Yorkshire |
The Opportunity
Join a newly formed AI team acting as an internal incubator for a highly established, global media and technology group. We are reinventing how we operate through the lens of artificial intelligence, building high-impact solutions across our portfolio of digital publishing, visual content, and data analytics brands.
This role exists to bridge the gap between emerging AI research and tangible business value. We aren't interested in traditional, slow-moving development cycles; we are looking for a founding engineer to define what "AI-native" means for our entire organisation.
You will be forward-deployed, working directly with business units to solve problems that others haven't even identified yet. This is your chance to move from concept to working prototype in days, leveraging the most advanced agentic tools to ship at unprecedented velocity. You'll enjoy the autonomy of a startup founder, backed by the resources, scale, and rich data of a heritage media institution.
What You’ll Be Doing
Core Engineering & Prototyping
- Ship at Velocity: Build AI-powered solutions rapidly using AI-native development approaches, leveraging tools like Claude Code and agentic engineering techniques to build in hours what traditionally takes days.
- Embedded Prototyping: Build rapid proof-of-concepts directly alongside business teams. Move from requirements to working demonstrations with astonishing speed using TypeScript, Node.js, and AWS.
- Continuous Experimentation: Push technical boundaries by testing new models, techniques, and agentic approaches the day they are released.
- Architect for Scale: Partner closely with the Product Owner to translate validated experiments into elegant architecture, ensuring rapid prototypes evolve into scalable production systems.
Strategic Impact
- Multiply Capabilities: Create APIs, integration patterns, and developer experiences that allow other engineering teams to leverage your AI capabilities seamlessly.
- Establish Guardrails: Design and implement lightweight, pragmatic technical standards for responsible and ethical AI deployment without sacrificing development velocity.
- Lead by Example: Establish AI-native engineering practices that accelerate the entire organisation, leading through demonstration and shipping working code rather than writing lengthy documentation.
What We’re Looking For
Essential Skills
- Modern Engineering Depth: Senior-level expertise in TypeScript and Node.js with a proven ability to architect, deploy, and scale production systems in AWS cloud environments.
- Advanced AI Fluency: Deep experience building agentic systems, implementing complex RAG architectures, and chaining multiple LLMs (OpenAI, Anthropic, etc.). You go far beyond basic API wrappers and understand context management, prompt engineering, and model behavior.
- AI-Assisted Workflow: You already use AI coding assistants (Claude Code, Copilot, Cursor) to accelerate your own engineering workflow.
- Rapid Iteration: Comfort operating at extreme velocity in ambiguous environments. You know intuitively when to hack together a messy prototype for learning versus when to build robustly for production scale.
- Collaborative Mindset: Strong stakeholder communication skills. You can naturally gather requirements from non-technical users and explain technical trade-offs without condescension.
Desirable Skills
- Domain Context: Experience working in media, content licensing, data-intensive domains, or high-volume publishing.
- Infrastructure Expertise: Deep understanding of modern cloud infrastructure, including containerisation, serverless architectures, and Infrastructure as Code (IaC).
- ML Intuition: A background in data science or ML engineering that provides intuition for embedding spaces, attention mechanisms, and evaluation methodologies.
- Production Observability: Experience with monitoring and telemetry tooling to debug complex, multi-step AI agents in production.
- Community Presence: Active contribution to open-source AI projects or a technical footprint demonstrating thought leadership in the AI engineering space.