MLOps Architect

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 an MLOps Engineer, you own the path from model prototype to reliable production on Azure and Databricks. You design and operate the systems that support training, deployment, monitoring, governance, and retraining, ensuring that AI and ML solutions remain reliable, scalable, secure, and trustworthy in production.

You combine hands-on MLOps engineering with solution architecture. You can assess workloads, identify bottlenecks, design the target approach, and turn technical requirements into an actionable delivery proposal. You work closely with the AI Solutions Architect, AI Engineers, Data Engineers, and platform teams, and you are accountable for the quality of AI/ML delivery.

The client cloud landing zone and core data platform are typically already in place. Your role is to integrate with and build on that foundation rather than design it from scratch. Data Engineering owns ingestion and curation through the silver or gold layer; MLOps ownership begins with feature pipelines and the production ML lifecycle built on top of those curated datasets.

Your responsibilities will include:

• Design 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.