Content Designer in Birmingham

Pontoon is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone’s chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive.

Contract: 6-Month Contract

Location: London (2 days in office)

Rate: £500 - £550 inside umbrella

About the role:

We’re looking for an agentic content designer. You’ll be responsible for shaping customer-first experiences in the Conversational Banking lab, bringing strong content design fundamentals and design thinking to emerging AI-powered work — including the content and knowledge foundations that make agentic systems trustworthy. This role sits at the intersection of content design, conversation design, and AI orchestration. You’ll use design thinking to understand customer needs, explore options, and iterate with a cross-functional team so that generative models and agentic systems deliver safe, compliant, life‑centred and outcomes‑focused experiences — grounded in searchable, accurate, well‑structured, machine‑usable content.

You’ll build on traditional content design foundations and apply them to new problem spaces, such as probabilistic content, knowledge and grounding content, and prompt and response governance. You’ll also help shape how content is chunked and componentised, tagged with metadata, kept consistent in terminology, and governed (ownership, version control and review cycles) so outputs can be traced back to trusted sources. Prior experience designing for AI is ideal, but curiosity, fast learning, and a desire to explore are essential.

Traditional content designers operate at journey or UI copy level – agentic designers shape the ecosystem.

Overview of the role and expectations:

Day-to-day you’ll be immersed in the cross-functional design team, working closely with engineering, product and design colleagues to test, iterate and improve the agentic experiences you’re designing - and partnering with data science/engineering on the content structures and pipelines that help AI systems use trusted information well.

As an experienced practitioner confident in designing deterministic experiences, you’ll be at the point in your career where you’re ready to step into agentic design, if not already in that space. You’ll have a forensic understanding of customers’ needs, together with a relentless curiosity to apply this to human-AI integration and co-agency in a financial services landscape. As a go-getter, you’ll be someone who brings agility to how you work each day, comfortable with ambiguity and able to navigate complexity.

Key Responsibilities:

• Apply core content design and design thinking methods to define problems, map journeys, and design customer-first outcomes for conversational and agentic experiences.
• Create and evolve content approaches for emerging AI patterns (for example: model answers, grounding/knowledge content, as well as prompt and response governance). As the work develops, you’ll be ensuring content is structured so AI can reliably interpret and use it.
• Partner with data science and engineering to define and evolve a content ontology / knowledge graph that models relationships across products, journeys and internal systems, including rules for prioritising content sources and layers.
• Run or support content audits to locate relevant content sources and assess AI readiness (structure, accuracy, metadata coverage, duplication and staleness) in preparation for customer-facing agentic systems.
• Work with AI engineers to test and iterate on retrieval performance, refining chunking strategy, metadata filters and semantic search configuration to improve grounding quality and reduce failure modes.
• Help establish practices that reduce content debt over time - including deduplication, clear ownership, and traceability so AI outputs can be audited back to source content when needed.
• Help design scalable processes for content ingestion from multiple sources, including automated pipelines for content cleaning, normalisation, and quality checks.
• Work closely with product, engineering, risk, legal, researchers and subject-matter experts to ensure experiences are accurate, accessible, on-brand, safe and compliant.
• Prototype, test, and iterate using evidence, feedback and data to continuously improve end-to-end experience quality in a probabilistic environment.
• Help establish and improve ways of working, standards and evaluation practices for AI-enabled content design across the team.

Required skills and capabilities:

• Content design expertise: strong UX writing and microcopy skills, inclusive language, and the ability to make complex information clear and usable.
• Design thinking: experienced in problem framing, ideation, prototyping, testing, iteration, and using evidence to improve experiences.
• Systems mindset: able to design content across journeys and ecosystems (not just individual screens) and work confidently with ambiguity and evolving requirements.
• Collaboration: comfortable partnering with product, engineering, design, research, policy, legal/risk and subject-matter experts; able to explain and defend decisions.
• AI and conversation design awareness (ideal): familiarity with how generative AI and agentic patterns work, common failure modes, and how content can guide or constrain behaviour.
• Content architecture & structured content: experience creating and maintaining content models, taxonomies, metadata and/or ontologies so content can be found, interpreted and reused across channels and by AI systems; understands principles like chunking/componentisation, controlled vocabulary, and semantic relationships.
• Data-fluent collaboration: able to work effectively with data science and engineering on content pipelines (ingestion, cleaning, normalisation and transformation), and to define requirements and acceptance criteria for structured content used in AI - including quality checks, versioning and source traceability.
• Quality and risk awareness: understand the importance of accuracy, tone, safety and compliance in regulated environments; aware of AI failure modes (for example hallucination or retrieval failure) and the value of strong content governance and audit trail/traceability in mitigating risk.