AI Platform Architect (Databricks)

Excerpt from the original posting

Visium combines strategy, frontier AI, and deep regulated-industry expertise to build and deploy AI-native systems that reinvent how businesses operate - unlocking the value of AI at scale. 

We're not a strategy firm that ships slide decks and walks away. Not a tech consultancy that builds what won't get adopted. Not a product company chasing problems it doesn't understand. We're all three at once: strategy, deep technical and integration expertise, and product, working as one. That combination is our flywheel. Consulting keeps us close to the hardest real-world problems. Products let us solve them at scale. Every engagement sharpens our products; every deployment makes the next engagement faster, deeper, and more certain.

We're obsessed with outcomes. While 95% of enterprise AI pilots never reach production, Visium is built to be the 5% delivering productised AI that compounds in value, not point solutions that deliver marginal gains. We are AI-native, not AI-adjacent. We don't bolt AI onto broken workflows; we redesign them with AI at the core.

Role 
As AI Platform Architect, you own the AI and ML layer of the Databricks platforms Visium builds for clients on Azure: feature pipelines, model lifecycle, serving, monitoring, governance, and the templates AI Engineers use to reach production. You build the platform underneath AI use cases, not the use cases themselves, so that the solutions built on it remain reliable, scalable, secure, and trustworthy in production.
You combine hands-on ML platform engineering with solution architecture, bringing the experience of having designed and built such platforms before. You shape the architecture with client leadership, assess workloads, identify bottlenecks, and turn technical requirements into a target design and an actionable delivery proposal. You work closely with AI Solutions Architects, AI and ML Engineers, Data Engineers, and platform teams.

Responsibilities: 
- Design, stand up and operate ML platforms on Azure Databricks, including feature pipelines and feature management, MLflow experiment tracking, model registry, training pipelines, and batch or online serving.
- Define and implement model lifecycle standards covering versioning, reproducibility, evaluation gates, promotion, deployment, rollback, and retraining.
- Build production-ready deployment patterns, including models exposed through APIs, automated MLflow workflows, and CI/CD pipelines for models and data.
- Own production monitoring for ML…

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