Engineering Manager - Data & AI

🚀 Become our new Engineering Manager – Data & AI (all genders)

As an Engineering Manager – Data & AI, you are the system and outcome owner for complex, data-driven and agentic solutions. You hold end-to-end responsibility for technical conception, architectural decisions, implementation depth, and the real, measurable business impact delivered to our clients.

This role is deliberately forward-deployed: close to the business problem, close to stakeholders, and close to the engineering work. You combine senior engineering judgment with clear technical decision authority, people leadership, and accountability for delivery outcomes.

You are not a pure people manager nor a detached architect. You are a hands-on technical leader who ensures that systems are coherent, production-ready, and valuable in real-world enterprise environments.

Your Responsibilities

System & Architecture Ownership

• Design, evolve, and take ownership of complex Data & AI systems, including agentic architectures, orchestration patterns, integrations, and governance mechanisms.

• Define architectural guardrails, technical standards, and system-level trade-offs across scalability, cost, reliability, and maintainability.

• Identify and manage failure modes, fallback strategies, safety boundaries, and human-in-the-loop concepts for production-grade AI systems.

• Ensure that individual technical contributions come together into a coherent, consistent, and operable end-to-end system.

End-to-End Outcome Accountability

• Own the technical feasibility, implementation quality, and achieved business impact of delivered solutions.

• Make and defend sound engineering and architecture decisions under technological, domain, and organizational uncertainty.

• Ensure that responsibility does not stop at design or deployment, but extends to stable operation and sustained customer value.

Engineering-Driven Delivery & Hands-On Contribution

• Actively contribute to system design, agent design, protocols, and integrations — this is not a pure steering or review role.

• Co-own and prioritize the engineering backlog, translating business goals into clear technical user stories together with Senior Engineers.

• Sequence and steer implementation based on system dependencies, risk, and outcome impact.

• Spend a significant portion of time embedded in projects, balancing leadership with hands-on technical involvement.

People Leadership & Team Development

• Lead, mentor, and develop a team of senior engineers, domain engineers, and data experts.

• Foster a culture of ownership, technical excellence, and system thinking.

• Partner with People Experience to shape career paths, mentoring, performance development, and capability growth.

• Recruit, retain, and grow top-tier engineering talent in Data, Cloud, and AI domains.

• Act as a role model for technical responsibility, clarity in decision-making, and outcome focus.

Standards, Reuse & Productization

• Ensure the use and continuous improvement of existing frameworks, templates, and architectural standards.

• Identify and drive reusable system, agent, and architecture patterns beyond individual projects.

• Contribute to the long-term productization and scalability of KI performance’s Data & AI offerings.

Go-Live Readiness, Stability & Operations Alignment

• Own go-live readiness, stabilization phases, and the technical transition into sustainable operations.

• Work closely with Cloud AI Architects and QA Engineers to align on reliability, security, cost control, monitoring, and quality gates.

• Decide when systems are production-ready based on business-critical quality criteria.

Business Development & Client Ownership

• Act as technical owner and trusted partner for key customer accounts and strategic engagements.

• Support pre-sales and business development by shaping technical solution concepts, architectures, and delivery approaches.

• Contribute to effort estimation, feasibility assessments, and technical risk evaluation in proposals.

• Build and maintain long-term client relationships, managing expectations and ensuring sustainable value delivery.

• Represent KI performance as a credible technical authority in customer workshops, decision forums, and executive discussions.