Job Description

About the opportunity

You'll be joining a company whose AI products are used at scale — processing millions of documents, structuring unstructured data, and powering decisions across an entire industry. This is not a coordination role. You will sit at the centre of product delivery, working daily alongside ML engineers and data scientists to shape how AI systems are built, evaluated, and continuously improved.

The team is ambitious, technically strong, and ships with real autonomy. If you want ownership over something meaningful in AI — not just managing a backlog, but actively influencing model quality and product direction — this is the kind of role that's hard to find.

Project & context

The product domain centres on AI-powered information extraction and structuring — parsing, understanding, and normalising data from complex, high-volume sources. Think intelligent document processing at scale: CV parsing, skill taxonomy mapping, job classification, and entity resolution applied to real-world, messy data.

You'll manage multiple concurrent initiatives across a cross-functional team. Priorities span model improvement cycles, experiment design, scalability, and customer-facing quality — all of which you'll balance without a rigid playbook telling you how.

What we're looking for — Required

• 4+ years of product ownership in complex technical environments — you're the go-to expert on your product domain, not a coordinator between others
• Hands-on AI/ML product experience — you understand how models are trained, evaluated, and improved; you can interpret experiment results and prioritise model iterations meaningfully
• Comfortable leading backlog refinement and sprint planning across multiple high-complexity initiatives simultaneously
• Technically grounded enough to engage engineers as a peer you can spot constraints, challenge estimates, and earn trust without being a developer yourself
• Strong prioritisation under ambiguity — you make calls, take ownership, and keep the team anchored to real-world outcomes
• Experience collaborating with data scientists on experiment design, metric definition, and result interpretation
• Actively using AI tools to accelerate your own work, while maintaining full accountability for output quality
• English at professional written and spoken level — required

AI/ML product ownership Agile / sprint planning Cross-functional collaboration English — professional 4+ years PO experience

Nice to have — Preferred

• Familiarity with information retrieval systems such as Elasticsearch 8.x or OpenSearch 2.x
• Exposure to taxonomies, ontologies, or graph databases (e.g. Neo4j, RDF/OWL-based models)
• Background in staffing, recruiting, or HR tech — understanding the industry context adds real product depth here
• Awareness of AI compliance and governance considerations within product development (e.g. EU AI Act implications, bias/fairness auditing)
• Prior work with ATS or recruitment technology platforms at a product or integration level

Elasticsearch 8.x / OpenSearch 2.x Graph databases HR tech / ATS AI compliance / EU AI Act Ontologies / taxonomies