AI Platform Architect (Databricks)

Visium SA; Zürich; On-site; CHF 140,000 - 210,000; Full time

Benefits offered by this job

Education budget

Sport budget

Flexible culture

Global team

Job summary

Visium SA in Zürich, Switzerland is seeking an experienced AI Platform Architect to own the ML and AI layer of Databricks platforms built for clients on Azure. You will design feature pipelines, model lifecycle, serving, monitoring, governance, and reusable templates that accelerate production deployments.

You combine hands-on ML platform engineering with solution architecture, shaping designs with client leadership, assessing workloads, and turning requirements into actionable delivery

Qualifications

• 5+ years in MLOps, ML platform engineering, or similar, with hands‑on production ML experience.
• Proven experience designing and standing up an ML/AI platform.
• Proven experience taking models to production with deployment, monitoring, and lifecycle management.
• Deep hands‑on Databricks expertise (MLflow, Unity Catalog, Model Serving).
• Software‑engineering skills for MLOps, including packaging, APIs, and automation.
• Strong knowledge of containers and model serving patterns; Kubernetes/AKS beneficial.
• Cloud networking and security knowledge (IAM, encryption, observability).
• Understanding of ML governance, lineage, and model documentation.
• GenAI technologies experience (Azure OpenAI/Azure AI Foundry, RAG, vector search).
• Ability to influence senior stakeholders and collaborate across AI, data and platform teams.

Responsibilities

• Design, stand up and operate ML platforms on Azure Databricks with feature pipelines and MLflow tracking.
• Define and implement model lifecycle standards including versioning, evaluation gates, promotion and retraining.
• Build production‑ready deployment patterns and CI/CD pipelines for models and data.
• Own production monitoring for ML systems including data drift, model quality, latency and cost.
• Design scalable Databricks workloads and translate findings into clear designs and delivery proposals.
• Lead decisions on ML tooling and platform evolution, with Databricks as primary platform.

Skills

MLOps

ML platform engineering

Databricks (MLflow)

Azure Databricks

CI/CD pipelines

Terraform / IaC

Kubernetes / AKS

Cloud security / IAM

GenAI knowledge

Stakeholder communication

Tools

Terraform

CI/CD tooling

Azure OpenAI

Job description

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.

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.

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.