Data Platform Engineer (AI Platform) in London

Location: UK (preferred), the Netherlands, or Belgium

Are you a Data Platform Engineer who thrives on building reliable, scalable platforms for data and AI workloads? Do you enjoy working at the intersection of platform engineering, DataOps, and MLOps while ensuring that AI systems run securely and at scale?

We are looking for a Data Platform Engineer (AI & ML Infrastructure) to take ownership of the reliability, scalability, and observability of our global data platform. In this role, you will be responsible for the operational backbone of our Headless Data Architecture (HDA), ensuring that data and AI pipelines run seamlessly across regions and environments.

Your impact:

• You play a critical role in helping us achieve our goal of operating as an AI-powered Workforce-as-a-Service platform.
• You ensure that the underlying data and AI infrastructure is stable, scalable, and ready to support advanced analytics and AI use cases across Europe, the UK, the US, and APAC.
• You are responsible for the day-to-day reliability of the platform—monitoring pipelines, resolving incidents, and continuously improving performance and resilience.
• You drive the platform's evolution through automation, infrastructure-as-code, and cost optimization.
• You work on a modern data and AI stack built on Databricks and Azure, managing everything from data ingestion pipelines and governance in Unity Catalog to platform-wide observability.
• You ensure that data flows—from raw ingestion to AI-serving layers—are robust, traceable, and production-ready.
• You support model evaluation frameworks, manage the prompt lifecycle, and implement safeguards to ensure that AI systems are safe, reliable, and compliant in production.
• You work closely with data engineers, AI/ML engineers, and governance stakeholders to ensure that the platform is not only high-performing, but also future-proof, secure, and scalable.

What you will do:

• Operate and maintain the end-to-end data and AI platform on Databricks and Azure.
• Monitor, troubleshoot, and resolve production issues across data and ML pipelines.
• Manage Unity Catalog governance, access control, and data sharing structures.
• Build and maintain data ingestion and integration pipelines (e.g., using SnapLogic).
• Implement observability frameworks using tools such as OpenTelemetry and Grafana.
• Automate infrastructure provisioning using Infrastructure-as-Code (e.g., Terraform).
• Optimize compute usage, scaling policies, and overall platform cost efficiency.
• Support AI/ML evaluation frameworks and model validation pipelines.
• Manage the lifecycle, versioning, and deployment of prompts across environments.
• Implement AI guardrails, safety layers, and monitoring for production systems.
• Contribute to internal tooling and platform acceleration initiatives.
• Collaborate across data, platform, and AI teams to continuously improve the platform.

About the role:

You will be part of a growing Data & AI division that plays a central role in the transformation of HeadFirst Group x Impellam into a truly data-driven organization. This role is central to that ambition—ensuring that everything built on top of the platform, from analytics to AI products, is reliable, secure, and scalable. You work with modern technologies such as Databricks, Azure, and advanced observability tools, while contributing directly to the next generation of AI-enabled platform engineering.

What we expect from you:

What you bring:

• You are a hands-on Data Platform Engineer with a strong focus on reliability, automation, and scalability of data and AI infrastructure.
• 3–5+ years of experience in platform engineering, DataOps, or MLOps.
• Extensive experience with Databricks (cluster management, jobs, Unity Catalog, Delta Lake).
• Experience with Azure (ADLS, networking, identity, cost management).
• Experience with integration platforms such as SnapLogic or similar iPaaS tools.
• Solid experience with Infrastructure-as-Code (Terraform or equivalent).
• Strong knowledge of observability tools (OpenTelemetry, Grafana, or similar).
• Experience with CI/CD pipelines and automated deployments.
• Strong Python and/or Scala skills for automation and pipeline scripting.
• Understanding of data quality frameworks and monitoring.
• Experience with AI/ML platform concepts such as model evaluation, deployment, or safety measures.

What you offer as a professional:

• A reliability-first mindset — you prioritize uptime, stability, and data quality.
• Strong ownership — you take responsibility from the moment an incident occurs until it is resolved.
• A collaborative approach involving data, platform, and AI teams.
• A pragmatic approach that balances performance, cost, and scalability.
• Clear communication — able to explain platform risks and status to stakeholders.