Lead Architect

About

The Lead Architect plays a pivotal role at vector8. You will define, design, and build enterprise‑grade data and AI solutions for some of the most complex organisations in Switzerland. You are not only an architect, but you are also a hands‑on solution designer and occasional coder who shapes systems and guides teams through implementation. You bring deep expertise in enterprise and solution architecture for data and AI, combined with the willingness and ability to dive into code, validate architectural decisions, and help delivery teams move forward. You are comfortable navigating complex system landscapes, legacy environments, and strict requirements around security, compliance, and data protection. In close partnership with vector8’s Principal Cloud Enterprise Architect, you co‑design the cloud, data, and AI foundations that enable organisations to scale AI from individual use cases to a sustainable, enterprise‑wide capability. The role is based primarily in Switzerland, with travel to client sites and collaboration with teams across Europe.

Job Requirements

• Extensive and demonstrable experience in data engineering, AI solution architecture, and enterprise architecture.
• Deep expertise in designing and building complex AI solutions (MLOps, streaming, APIs, orchestration).
• Strong hands‑on engineering skills – comfortable coding in Python, SQL, or similar languages and working with modern data/AI toolchains.
• Strong understanding of hybrid architectures combining on‑prem systems with cloud data and AI services.
• Experience with at least one cloud provider: AWS, Azure, or GCP; multi‑cloud familiarity is a big plus.
• Experience optimising cost, security, and scalability in cloud deployments.
• Knowledge of data governance, responsible AI, security frameworks, and operational controls, ideally coupled with experience in highly regulated industries with strict requirements for security, privacy, compliance, and data governance.
• Excellent stakeholder management and communication skills, able to influence both executives and engineering teams.
• Ability to convert complex challenges into clear architectural decisions and actionable delivery plans.
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics or a related field.
• Fully proficient in both German and English, with French considered a valuable plus.
• Willingness to travel within Switzerland and occasionally to European delivery hubs.

Job Responsibilities

Architect Design Enterprise Grade Data AI Solutions

• Create end‑to‑end architectures for data platforms, AI systems, analytics environments, and integration layers.
• Design solution components, including data pipelines, ML pipelines, model hosting environments, real‑time processing, and APIs.
• Ensure solutions meet enterprise requirements for performance, security, compliance, resilience, and maintainability. Knowledge of SRE concepts: SLIs/SLOs, incident management, error budgets, production monitoring.

Hands‑on Contributor

• Contribute directly to solution development:
• Writing design/architecture
• Writing code
• Prototyping components
• Validating technical approaches (when needed)
• Maintain and extend CI/CD and infrastructure
• Support teams during complex engineering tasks, unblock challenges, and ensure the final solution aligns with the architecture.
• Balance high‑level architectural thinking with pragmatic, hands‑on execution.

Data AI Solutions Guide Delivery Technical Leadership

• Lead cross‑functional delivery teams of data engineers, ML engineers, DevSecOps/MLOps and software engineers.
• Define technical workstreams, review code and designs, and ensure architectural coherence throughout the implementation.
• Provide coaching, mentoring, and thought leadership to elevate engineering quality and delivery excellence.
• Expertise in building cloud infrastructure using IaC: Terraform, Bicep, Cloud Formation.
• Strong DevSecOps skills: CI/CD, automated testing, security scanning, containerization.
• Experience deploying and operating AI systems in production: monitoring, observability, A/B testing.

Data AI Solutions Navigate Complex Systems Regulated Environments

• Work across intricate enterprise ecosystems with heterogeneous applications, distributed data sources, and legacy components.
• Identify modernisation paths and integration patterns that respect operational realities and long‑standing constraints.
• Embed data protection, cyber security controls, governance, and compliance in solution design.

Enable AI at Scale

• Design architectures that support AI development, deployment, monitoring, governance, and life cycle management.
• Ensure alignment with cloud foundations and hybrid environments, toge