AI Data Architect – Distributed Cloud

About the role

We are looking for a seasoned Data & AI Architect to lead the design, governance and delivery of our distributed cloud‑based AI and machine‑learning ecosystem. The role combines deep data‑architecture knowledge with MLOps expertise to build reliable, scalable solutions that integrate with core business systems.

Key responsibilities

• Define and govern the end‑to‑end MLOps strategy, covering experimentation, training, versioning, deployment and monitoring of models.
• Design and implement cloud‑native, Kubernetes‑based infrastructure for model training, serving and high‑performance inference.
• Collaborate with data‑engineering teams to build efficient feature stores, data‑labeling pipelines and fast data integration between warehouses/lakes and ML pipelines.
• Drive CI/CD automation for code and model deployment, ensuring model lineage, security and reproducibility.
• Evaluate and recommend new tools and platforms to optimise performance, cost‑efficiency and future growth.
• Provide technical leadership and establish governance for data quality, model auditing and responsible AI deployment.

Required profile

• 7+ years of progressive experience in Data Engineering, Software Architecture or ML Engineering, with a focus on production‑grade AI systems.
• Hands‑on expertise with MLOps principles, containerisation (Docker) and orchestration (Kubernetes).
• Deep knowledge of at least one major cloud platform (AWS, Azure or GCP) and its AI/data services.
• Strong understanding of data‑architecture principles, data‑warehouse/lake design and data‑governance.

Required skills

• Python programming
• MLOps tools and pipelines
• Docker
• Kubernetes (K8s)
• AWS, Azure, or Google Cloud Platform
• Vertex AI, SageMaker, Azure ML
• BigQuery or Synapse
• Feature Store technologies (e.g., Feast)
• Microservices and API design