AI Platform Architect (Databricks)

Employment: Full-time 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 systems, including data and concept drift, model quality, latency, reliability, and cost, with appropriate alerting and retraining triggers. Design scalable Databricks workloads, identify architectural and performance bottlenecks, and translate findings into clear solution designs and delivery proposals. Lead decisions on ML tooling, integration patterns, and platform evolution, with Databricks as the primary platform and Azure ML used where appropriate. Build reusable templates, reference implementations, and accelerators that enable AI Engineers to move solutions into production independently. Establish ML governance practices, including lineage, audit trails, model documentation, access controls, and compliance-ready operating processes. Support GenAI delivery when needed, including deployment and monitoring of RAG and LLM applications using Azure OpenAI or Azure AI Foundry, vector search, evaluation, and guardrails. Apply the client’s existing cloud, security, networking, and operating standards to ML systems and integrate with the established landing zone and data platform. Ensure ML platforms and workloads are reliable, cost-efficient, secure, maintainable, and observable. Act as lead architect on client projects and the main client-facing contact for technical platform questions, discussing architecture with client CTOs and technical leadership. Contribute to the technical part of proposals and RFPs, and shape Visium’s strategy for designing, deploying and managing AI and ML platforms. Partner with engineering teams and senior stakeholders to guide implementation, mentor contributors, and accelerate adoption, helping shape a growing team as one of its senior technical anchors. Requirements To succeed in this role, you bring a combination of expertise, experience, and skills including: 5+ years of experience in MLOps, ML platform engineering, or a similar role, typically building on earlier experience in data science or ML engineering, with significant hands-on experience running ML systems in production. Proven experience designing and standing up an ML or AI platform yourself, not only building on one designed by others. Proven experience taking models to production and keeping them reliable, with concrete examples covering deployment, monitoring, incident resolution, and lifecycle management. Deep hands-on expertise in Databricks, especially MLflow, Unity Catalog, Model Serving and Asset Bundles. Azure Databricks is strongly preferred. Experience with Azure ML is a strong advantage. Strong experience designing Databricks solutions, diagnosing workload bottlenecks, and producing clear technical designs and implementation proposals. Software-engineering skills for MLOps and platform work, including packaging, testing, APIs, and maintainable automation. Deep experience building CI/CD pipelines for models and data, beyond infrastructure and application deployment alone. Strong knowledge of containers and model-serving patterns. Experience with Kubernetes or AKS is beneficial but not required. Confidence with Terraform and Infrastructure as Code, particularly when integrating ML services into an existing Azure landing zone. Working knowledge of cloud networking and security, including IAM, encryption, secrets management, governance, and observability. Understanding of ML governance, lineage, auditability, and model documentation. Working knowledge of GenAI technologies and patterns, including Azure OpenAI or Azure AI Foundry, RAG, vector search, evaluation, and guardrails. Ability to influence senior stakeholders, communicate technical strategy clearly, and collaborate effectively across AI, data and platform teas. Client-facing experience, for example at a consultancy or technology vendor, is a strong advantage. Strong mentoring skills and a pragmatic approach to helping engineering teams deliver independently. Motivation for client-facing consulting work across several clients, including business travel. From the outset, you can independently: Deploy a model behind a production-ready API or managed serving endpoint. Configure MLflow experiment tracking and model registration. Build or improve a CI/CD pipeline for models and data. Assess an ML workload, identify bottlenecks, and produce a sound solution design and delivery proposal. Over time, you establish repeatable standards and platform capabilities that make ML delivery faster, safer, and more reliable across teams. You take on the lead architect role on client engagements, support presales, and shape Visium’s approach to AI and ML platforms. Benefits What we offer A competitive compensation package A yearly education budget to steep your learning curve A yearly sport budget because a fit body leads to a fit mind A flexible working culture because your work-life balance matters to us A position that enables you to have an impact on 1’000s of people, and the whole company's growth. An international, knowledgeable, and passionate team with a strong collaborative mindset Check our LinkedIn and website to learn more about us & don’t hesitate to contact us if you have any questions.