Manager Agentic AI

Tasks

• Lead and develop the Agentic AI engineering team through clear goals, regular feedback, coaching, performance management, career development, and thoughtful allocation of ownership.
• Contribute directly to the design, implementation, and operation of substantial areas of Esko's shared Agentic AI platform.
• Help shape the platform's technical direction, architecture, interfaces, and engineering quality bar in collaboration with the wider platform and architecture community.
• Translate recurring product and engineering needs into reusable platform capabilities, while accommodating legitimate domain differences without creating product-specific forks.
• Establish strong engineering practices for reliability, security, observability, evaluation, performance, and compatibility for consuming teams.
• Work directly with product engineering teams to support adoption of the shared platform and help them take domain-specific agentic capabilities from exploration into production.
• Create an environment in which engineers can experiment, learn, challenge assumptions, and make evidence-based technical decisions while remaining accountable for production outcomes.
• Define and use meaningful measures of platform adoption, quality, operational performance, delivery effectiveness, and team development to guide priorities and improvement.

Conditions & benefits

Requirements

• Strong production software engineering experience in Python, TypeScript, Java, or a similar language, including designing, building, and operating reliable systems.
• Demonstrated experience leading and developing engineers, including line management, coaching, performance feedback, and creating meaningful technical ownership.
• Experience building a platform, API, or shared service used by multiple teams, with sound judgement about abstractions, interfaces, compatibility, and operational responsibilities.
• Experience designing or building production AI systems using large language models, tool use, or agentic workflows, together with a practical understanding of their limitations and risks.
• A track record of taking uncertain or early-stage technical ideas through evaluation, implementation, and adoption as dependable production capabilities.
• Ability to balance platform coherence with legitimate product and domain differences, and to make clear technical trade-offs when needs conflict.
• Experience collaborating across product engineering, architecture, infrastructure, security, and business functions in a global organization.
• Excellent written and verbal communication, with the ability to explain technical decisions clearly to engineering, product, and leadership audiences.
• Undergraduate, Master's degree or PhD in Computer Science / Machine Learning / Data Science / Artificial Intelligence, or related disciplines.