Software Engineer - AI
Role details
Job location
Tech stack
Job description
- Build rapid proof-of-concepts embedded directly with business teams - moving from requirements to working demonstration with astonishing speed, using technologies such as TypeScript/Node.js/AWS to create solutions users can touch and test immediately in their actual working environment.
- Push technical boundaries through continuous experimentation with emerging AI capabilities - you're always testing new models, techniques, and agentic approaches, determining what's hype versus what unlocks genuine business value before the competition recognises the opportunity.
- Partner closely with the Product Owner - AI to translate validated opportunities into technical reality, turning business requirements into elegant architecture whilst ensuring rapid prototypes can evolve into production systems that scale.
- Engineer sophisticated solutions using modern AI platforms and APIs - building agentic systems, implementing RAG architectures, and creating multi-step workflows that leverage OpenAI, Anthropic, and emerging providers to deliver capabilities impossible six months ago.
- Enable business success by making AI accessible - creating APIs, integration patterns, and developer experiences that allow other teams to leverage your AI capabilities without needing your deep expertise, multiplying impact across the organisation.
Strategic Contributions
- Establish AI-native engineering practices that accelerate the entire organisation - demonstrating how tools like Claude Code transform development velocity, creating reusable agentic patterns, and building technical capability that didn't exist before.
- Design and implement lightweight technical standards for responsible and ethical AI deployment - defining pragmatic guardrails that protect quality without killing velocity, establishing evaluation frameworks that enable rapid iteration with appropriate risk management.
- Build a library of proven technical patterns and reusable components - creating the building blocks that turn weeks of custom development into days of configuration, accelerating every subsequent AI implementation across the portfolio.
- Lead through demonstration rather than documentation - shipping working examples that other engineers can learn from, running technical deep-dives that elevate team capability, and establishing engineering culture through visible excellence.
- Stay connected to the bleeding edge of AI engineering - actively participating in technical communities, experimenting with research releases, and maintaining relationships with AI platform providers that give PA Media Group early access to emerging capabilities.
- Contributing to wider PA Media Group technology approaches and capabilities to drive forward mutual progress and success.
Requirements
Do you have experience in TypeScript?, * Senior-level engineering expertise in TypeScript/Node.js with proven ability to architect and ship production systems in AWS cloud environments - you write exceptional code, understand system design deeply, and have the battle scars from scaling solutions that matter.
- AI-native engineering fluency that goes far beyond API consumption - you're already using Claude Code or similar tools to accelerate your own development, you build agentic systems that chain multiple capabilities, and you understand prompt engineering, context management, and model behaviour at a sophisticated level.
- Solid understanding of ML/AI fundamentals that informs excellent engineering decisions - you grasp concepts like embedding spaces, attention mechanisms, RAG architectures, and fine-tuning well enough to choose appropriate approaches without needing to implement models from scratch.
- High energy and relentless curiosity that drives continuous experimentation - you're naturally drawn to emerging capabilities, you experiment with new models the day they're released, and you're constantly asking 'how might we?' when encountering novel business problems and technology.
- Comfort operating at extreme velocity in ambiguous environments - you thrive when requirements evolve hourly, make confident decisions with incomplete information, and know intuitively when to optimise for learning versus building for scale.
- Strong stakeholder collaboration skills that enable effective forward deployment - you gather requirements from non-technical users naturally, explain technical trade-offs without condescension, and build trust through delivering working solutions that exceed expectations.
- Senior-level judgement about when to push boundaries versus when to leverage proven patterns - you experiment aggressively but ship responsibly, understanding the difference between impressive demos and production-ready systems.
Desirable Skills
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Experience working in media, content, or data-intensive domains where you've encountered challenges of scale, quality assurance, and maintaining trust at speed.
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Deep understanding of modern cloud infrastructure (AWS, Azure, GCP) and deployment patterns including containerisation, serverless architectures, and infrastructure as code.
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Background in ML engineering or data science that provides intuition for model behaviour, training dynamics, and evaluation methodologies beyond surface-level API usage.
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Experience with observability and monitoring tooling that enables rapid debugging of complex agentic systems where behaviour emerges from multiple AI components interacting.
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Familiarity with data pipeline architectures and integration patterns that enable sophisticated AI applications built on top of existing data infrastructure.
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Active participation in AI engineering communities - contributing to open-source projects, sharing learnings publicly, or maintaining technical blog/social presence that demonstrates thought leadership.