Senior AI Platform Engineer -
Knowledge & Reasoning
Location: Flexible - remote, hybrid, or based at an Esko location.
Worldwide locations are considered.
Function: Software Engineering
Reports to: Team Leader, Esko AI
The Esko AI team is a group of specialists focused on leveraging AI to solve valuable and often challenging customer problems that have historically resisted automation via purely rule-based approaches. To succeed, we must ingest and leverage expert knowledge to augment the capabilities of our AI agents and services. This capability will be a key part of Esko’s centralized AI platform, enabling wider teams to build AI into their products using a common base.
Packaging workflows are governed by knowledge of very different kinds: regulatory requirements (FDA, EMA, QRD), technical specifications, brand guidelines, customer-specific conventions, and practices that currently live in the heads of experts. Some of this knowledge is binding, some advisory, some contradictory, and much of it applies only to a particular customer, brand, or specific range of designs.
For AI systems to operate reliably in this environment, they must do more than retrieve relevant information. They need to determine which knowledge applies, distinguish mandatory requirements from guidance, handle missing or conflicting evidence, and recognize when human judgement is required.
We are building knowledge and reasoning as a shared capability of Esko's AI Platform, enabling product teams to create AI-powered workflows grounded in trusted, contextual knowledge. As part of the platform team, you will help shape and build this capability, taking end-to-end ownership of substantial technical areas.
Design the knowledge representation: how constraints, transformations, procedures, and guidelines are modelled so that both agents and deterministic validators can use them, and so that humans can read, curate and correct them.
Build and operate platform services for ingesting, curating, retrieving, and versioning knowledge, ensuring it is scoped correctly to the relevant customer and workflow context.
Define how semantic retrieval, deterministic validation, and human review work together, including how applicability, uncertainty, conflicting evidence, and escalation are handled.
Build evaluation suites covering retrieval quality, applicability, conflict and ambiguity handling, and explanation quality, using measurable results as the primary evidence of progress.
Take end-to-end ownership of substantial technical areas, including their security, tenant isolation, reliability, observability, performance, and compatibility for consuming teams.
Work directly with domain experts and product engineering teams to turn informal expertise into structured, testable knowledge with clear provenance, citation, and release practices.
Strong software engineering experience designing, building, and operating production systems in Python, TypeScript, Java, or a similar language.
Experience building knowledge, retrieval, or rule systems that other teams depend on, and the judgement to choose the right formalism for a problem, whether that is a schema, decision table, rules engine, graph, or retrieval.
