Position Responsibilities

We are looking for an Engineering Manager to strengthen solution engineering, lead technical enablement for platform modernisation and build practical AI capability across ZUS Coffee. You will turn business requirements into implementable designs, support our Go and Node.js microservices initiative, and help engineers deliver reliable AI and automation solutions.Reporting to the Head of Engineering, you will combine hands-on technical leadership with management of a focused team as the function develops. You will work with Product, Data, Security, IT Operations and business stakeholders to improve solution quality and build capabilities that teams can reuse across markets.

Solution Engineering & Architecture

• Lead technical discovery and end-to-end solution design across consumer apps, backend services, ordering, payments, loyalty, POS and enterprise integrations. Translate requirements into designs, dependencies, effort estimates and delivery options.
• Evaluate build, buy and reuse options against business value, implementation risk, operating cost and maintainability. Assess vendor architecture, integration feasibility and knowledge-transfer requirements.
• Document key decisions, API and data contracts, and non-functional requirements. Design for security, privacy, performance, resilience and regional configuration needs.
• Validate important design assumptions through prototypes, code reviews and performance checks. Guide implementation and resolve complex architectural issues with the delivery teams.

• Provide technical leadership for Go and Node.js services, including service boundaries, API standards, asynchronous messaging and reusable service patterns.
• Guide incremental migration from existing platforms, addressing data ownership, consistency, backward compatibility, rollout and rollback so customer journeys remain dependable.
• Develop reference implementations and service templates with automated tests, CI/CD, observability and operational documentation. Coach teams to adopt and maintain them.
• Identify architectural debt and reliability or cost constraints, and agree practical improvements with the Head of Engineering and platform owners.

• Develop a prioritised AI capability roadmap with the Head of Engineering and CTO. Select use cases based on business value, data readiness, feasibility and ongoing cost.
• Lead selected AI and automation initiatives from discovery through pilot and production handover. Apply suitable approaches, including model APIs, retrieval, agents and conventional automation.
• Build reusable integrations between AI solutions and business services, using stable interfaces and appropriate interoperability standards. Preserve flexibility to change models or providers
• Establish practical evaluation and release criteria covering output quality, task success, latency, cost and failure handling. Include data-access controls, protection against prompt injection, limits on agent actions and human approval where needed.
• Introduce approved AI tools into development, testing and documentation. Measure their effect on delivery time and quality while maintaining code review, testing and protection of company information.
• Build internal competence through workshops, pairing and documented examples. Track adoption and ensure knowledge and operational ownership transfer to the teams maintaining each solution.

• Lead and develop a focused team as established, including clear objectives, coaching, performance feedback and participation in hiring.
• Own milestones, technical quality and risk management for assigned initiatives. Agree resources and sequencing with the Head of Engineering, and escalate blockers and trade-offs early.
• Mentor technical leads and engineers in solution design, backend engineering and AI practices. Delegate ownership so the organisation can apply these capabilities independently.
• Improve development workflows, test automation and engineering documentation with the relevant team leads. Measure outcomes such as lead time, escaped defects, reliability and operating cost.

• Run proportionate design reviews and maintain useful architecture records. Align standards with the CTO and Head of Engineering and support timely technical decisions.
• Partner with Security, Data and IT Operations on data handling, access controls, monitoring, recovery and production readiness. Ensure maintainers receive runbooks and clear support ownership.
• Provide architectural support for significant incidents and recurring problems, and help delivery teams implement lasting remediation.

• The Head of Engineering retains overall engineering delivery, staffing and portfolio accountability. This role owns solution quality, reusable technical capabilities and the outcomes of its assigned team and initiatives.
• Product and delivery teams retain their respective product and service responsibilities. Priorities and capacity are agreed with the Head of Engineering; routine sprint coordination and first-line production support remain with the designated delivery and operations teams.

Qualifications & Education

• Typically 7 or more years of software engineering experience, including technical leadership and experience managing or developing engineers.
• Strong practical backend engineering experience in Go and Node.js, including building, reviewing and supporting production services.
• Proven experience designing and delivering solutions across multiple systems, with sound judgement on microservices, API design, distributed systems, messaging and data consistency.
• Experience with cloud platforms, preferably AWS, and containerisation, CI/CD, automated testing, application security and observability.
• Practical experience applying AI or automation to software products or engineering workflows. Able to prototype integrations, assess limitations and define quality and operational controls.
• Ability to remain hands-on in design reviews, code reviews and prototypes while coordinating implementation and developing others.
• Clear communication with technical and business stakeholders, including estimates, trade-offs, costs and delivery risks.
• A degree in Computer Science, Software Engineering or a related discipline, or equivalent practical experience.

• Legacy modernisation and service migration in high-transaction or multi-market environments.
• Production AI integrations using model APIs, retrieval-augmented generation or agents.
• Measured improvements from AI-assisted development practices.
• Retail, F&B, e-commerce, payments, loyalty, POS or enterprise integrations, including vendor assessment and handover.