AI Agent Engineer

• Design, build and ship production LLM agents against real editorial, rights, archive and commercial workflows, from scoping through to deployment and monitoring
• Build the shared platform underneath the agents including orchestration, tool and API integrations, retrieval, prompt management, evaluation and observability
• Integrate agents with existing systems including the archive, submission portal, clips platform and internal editorial tooling
• Work directly with editorial, research and commercial teams to identify the workflows worth automating and define what ‘good enough to trust’ looks like for each
• Build evaluation and guardrails so agent output quality is measurable rather than anecdotal, with human review at the right points
• Own cost and latency, keeping model spend proportionate to the work being replaced
• Document and hand over so the platform is not a single-person dependency
• Keep an eye on the tooling landscape and make pragmatic build-versus-buy calls

• 3+ years commercial software engineering experience with strong Python
• Built and shipped at least one LLM-powered feature or agent into production and operated it afterwards (real, used side projects count)
• Practical grasp of the agent toolkit: function and tool calling, RAG, vector databases, structured output, prompt engineering and context management
• Comfortable with at least one agent framework or SDK, with an understanding of why the abstractions exist rather than blind loyalty to a specific one
• Solid API and integration experience, since much of this role is plumbing agents into systems that were not designed for them
• Cloud deployment experience (AWS, GCP or Azure) and standard engineering hygiene: version control, testing, CI/CD
• Able to talk to non-technical colleagues about what an agent can and cannot reliably do, and confident enough to push back when a request is a bad fit

Nice to have:

• Background in media, publishing, broadcast or content licensing
• Multimodal work: image or video understanding, embeddings, automated tagging or computer vision
• Experience building evals or LLM observability tooling
• Workflow or pipeline orchestration at volume
• Front-end capability for internal tools so users can see and steer what the agents are doing

This role would suit an engineer who wants genuine greenfield ownership of an agent platform, a varied problem space spanning editorial, rights, archive and commercial, and short feedback loops with the people whose work the agents actually change.

A global media and content business is hiring an AI Agent Engineer to build the first dedicated agent platform inside a high-volume, deadline-driven editorial and licensing operation.

Editorial teams across four continents source, clear, produce and distribute content seven days a week to over 1,500 clients, sitting on an archive of roughly one million photographs and 40,000 video clips. A lot of the work keeping that moving is repetitive and judgement-light and currently scales linearly with headcount. This role exists to change that. You’ll be the first dedicated hire on the agent platform, building a suite of AI agents that take on those repetitive layers so editorial and commercial teams can focus on the work that actually needs a human. This is a build role, not a research role, and success looks like agents running in production against real workflows, not prototypes.