platform engineer

kaiko.ai

<div>
 
 About Kaiko.ai
 
 
 
 Kaiko is building a next-generation agentic clinical AI assistant that helps clinicians reason across patient data, guidelines, and diagnostics.
 
 
 Healthcare decisions are rarely made by a single person or from a single data source. kaiko's assistant maintains longitudinal patient context across encounters, clinicians, and institutions, enabling collaboration, second opinions, and complex diagnostic workflows. The system is designed to operate safely in real clinical environments, with human oversight, auditability, and regulatory alignment at its core.
 
 
 Our assistant core supports broadly applicable clinical tasks such as patient data navigation, guideline interaction, multimodal interaction (chat and voice), and care coordination. On top of this foundation, we are developing specialized diagnostic agents in areas such as oncology, radiology, and pathology.
 
 
 We build in close collaboration with leading hospitals and research centers, including the Netherlands Cancer Institute (NKI). kaiko is a well-funded company with a growing international team, operating from Zurich and Amsterdam.
 
 
 
 About The Role
 
 
 
 As an ML Platform Engineer, you'll help architect, scale, and evolve the infrastructure that powers kaiko's foundation-model training and serving across the full ML lifecycle: from compute orchestration that runs large-scale training jobs, through experiment tracking and model registry, to GPU-backed serving in production.
 
 
 The Data &amp; AI Platform team owns the foundation underneath kaiko's training and serving workloads: the orchestration and distributed compute layer (Dagster + Ray on Kubernetes), the storage layer (Hammerspace), and the observability that keeps them reliable. We support both research, with large-scale training workloads on open-weight MoE models in the hundreds-of-billions to trillion-parameter range, and product, with the engineering teams building kaiko's clinical applications.
 
 
 Today, our platform is built around large-scale training. The next frontier is closing the loop: building the model lifecycle layer (experiment tracking, registry, versioning, and GPU-backed serving) that lets our trained models move reliably into product. You'll be part of the team that gets us there.
 
 
 You'll work closely with both research and product engineering to understand what the platform needs to deliver, and own the engineering of how. You'll contribute to the platform's evolution across both training and serving dimensions, helping translate team needs into reliable, scalable infrastructure.
 
 
 You will be based in either The Netherlands or Switzerland, with the expectation of spending at least 50% of your time at the office.
 
 
 
 Some areas of responsibility
 
 
 
 <ul>
 <li>
 Build and evolve the infrastructure that makes ML development fast, reliable, and observable: from IaC to CI/CD to Kubernetes-based workload orchestration.
 </li>
 <li>
 Contribute to the compute orchestration layer: help scale GPU workloads across heterogeneous on-prem and cloud clusters using Kubernetes, Ray, and advanced GPU scheduling technologies such as KAI-Scheduler.
 </li>
 <li>
 Support hybrid and multi-cloud strategies that balance performance, compliance, and cost, including the Hammerspace storage rollout.
 </li>
 <li>
 Own and evolve parts of the AI Factory: the Dagster-based orchestrator and its Ray integration that training jobs depend on.
 </li>
 <li>
 Build and maintain the model lifecycle layer: experiment tracking, model registry, versioning, and GPU-backed serving infrastructure, so models trained on our clusters move reliably into production.
 </li>
 <li>
 Partner with both research and product engineering to understand platform requirements and translate them into shared infrastructure that works across use cases.
 </li>
 <li>
 Bring engineering rigour to a fast-moving stack: lineage, reproducibility, ownership boundaries, and documentation that lets the team move quickly without losing track.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 2-5 years of experience in production ML platform engineering or ML Ops role.
 </li>
 <li>
 Hands-on experience with Kubernetes, Helm, Terraform, Docker, and CI/CD tooling (ArgoCD, GitHub Actions, or comparable). Comfortable with modern observability stacks (Grafana, Prometheus, Loki, or similar).
 </li>
 <li>
 Compute orchestration at scale. Production experience scheduling GPU workloads on Kubernetes, Ray, or comparable, or strong motivation to grow into this quickly.
 </li>
 <li>
 ML infrastructure exposure. Hands-on experience with Linux and NVIDIA GPU environments, including multi-node training stacks and the networking that connects them (InfiniBand or comparable). You have a feel for how researchers and engineers use the platform, not just how it's built.
 </li>
 <li>
 Familiarity with the full ML workflow: training runs, experiment tracking, model registry and versioning (MLflow or equivalent), and model serving. You understand how models move from training to production.
 </li>
 <li>
 Solid software engineering. Python as a baseline; openness to picking up Go or other programming languages where the platform demands it.
 </li>
 <li>
 Collaborative by default. You work across team boundaries (research, product, data engineering) and design interfaces that serve multiple stakeholders without requiring everyone to understand the underlying plumbing.
 
 
 </li>
 </ul>
 
 Nice to have:
 
 
 
 <ul>
 <li>
 Direct experience supporting large-scale foundation-model training or inference, with a working sense of how a run behaves from the platform's perspective: MFU, GPU utilisation, communication overhead, and dataloader stalls on the training side; serving frameworks (vLLM, Triton, TorchServe), KV cache management, and batching strategies on the inference side.
 </li>
 <li>
 Familiarity with lower-level GPU communication and I/O concepts: RDMA, GPUDirect Storage (GDS), GPUDirect RDMA, NCCL, and how these affect training throughput and cluster design.
 </li>
 <li>
 Experience with Kubernetes-native scheduling stacks for accelerator-heavy workloads, such as Volcano, KAI Scheduler, YuniKorn, or similar, so scheduler tradeoffs are understood rather than defaulted to.
 </li>
 <li>
 High performance (parallel) filesystems that feed GPU's with high throughput and low latency (Hammerspace, CEPH, WEKA, VAST).
 </li>
 <li>
 On-prem and hybrid GPU environments, especially in regulated settings (healthcare, finance, public sector).
 </li>
 <li>
 Exposure to MoE architectures or large-scale distributed training, useful for anticipating what future workloads will demand of the platform.
 
 
 </li>
 </ul>
 We are excited to gather a broad range of perspectives in our team, as we believe it will help us build better products to support a broader set of people. If you&rsquo;re excited about us but don&rsquo;t fit every single qualification, we still encourage you to apply: we&rsquo;ve had incredible team members join us who didn&rsquo;t check every box!
 
 
 
 Why kaiko
 
 
 
 At kaiko, we believe the best ideas come from collaboration, ownership and ambition. We&rsquo;ve built a team of international experts where your work has direct impact. Here&rsquo;s what we value:
 
 
 <ul>
 <li>
 Ownership: You&rsquo;ll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work.
 </li>
 <li>
 Collaboration: You&rsquo;ll have to approach disagreement with curiosity, build on common ground and create solutions together.
 </li>
 <li>
 Ambition: You&rsquo;ll be surrounded by people who set high standards for themselves and others, who see obstacles as opportunities, and who are relentless in their work to create better outcomes for patients.
 
 
 </li>
 </ul>
 
 
 
 In addition, we offer:
 
 
 
 <ul>
 <li>
 An attractive and competitive salary, a good pension plan and 25 vacation days per year.
 </li>
 <li>
 Great offsites and team events to strengthen the team and celebrate successes together.
 </li>
 <li>
 A EUR 1000 learning and development budget to help you grow.
 </li>
 <li>
 Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings.
 </li>
 <li>
 An annual commuting subsidy.
 
 
 </li>
 </ul>
 
 Our interview process
 
 
 
 Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps:
 
 
 <ul>
 <li>
 Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role.
 </li>
 <li>
 Coding assessment &amp; debrief: You'll complete a time-limited coding exercise, started at a time of your choosing. Afterwards, you'll join a live session with members of our team to walk through your solution, explain your reasoning, and discuss any trade-offs you made.
 </li>
 <li>
 Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario.
 </li>
 <li>
 Onsite meeting (optional): You&rsquo;ll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.
 </li>
 <li>
 Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.
 </li>
 </ul>
</div>

ML Platform Engineer

Sprint and Partners

<div>
 
 
 SRE - Data Platform Engineer - Kubernetes - top 0,5% biggest data set globally
 
 
 
 
 Amsterdam
 
 (Hybrid) - Visa sponsorship and relocation included
 
 
 
 
 
 Building the backbone of data &amp; AI at scale. This is not a &ldquo;keep the lights on&rdquo; role.
 
 
 
 
 
 If you are interested in:
 
 <ul>
 <li>
 Working on systems where failure is not an option
 </li>
 <li>
 A team that values engineering quality over shortcuts
 </li>
 <li>
 Real ownership &mdash; you build it, you run it, you improve it
 </li>
 <li>
 Growth paths in deep technical expertise or leadership
 </li>
 <li>
 A setup where your work has visible, measurable impact
 </li>
 </ul>
 
 
 
 
 Check this out:
 
 
 
 
 
 You&rsquo;ll be building and evolving the core platform that everything depends on&mdash;from data pipelines to machine learning and real-time AI services. Think high-scale Kubernetes, complex distributed systems, and real reliability challenges where your decisions actually matter.
 
 
 
 
 
 If you care about uptime, performance, and engineering systems that don&rsquo;t break under pressure, this is your kind of environment.
 
 
 
 
 
 Why this role stands out for SREs / Platform Engineers
 
 <ul>
 <li>
 Reliability is the product &mdash; not an afterthought
 </li>
 <li>
 Work on real-world scale problems (not toy infra)
 </li>
 <li>
 Balance batch, streaming, and real-time workloads in one platform
 </li>
 <li>
 Tackle non-trivial Kubernetes challenges (scheduling, multi-tenancy, resource isolation)
 </li>
 <li>
 Own systems that directly impact data, ML, and AI products
 </li>
 <li>
 High autonomy, high responsibility, low bureaucracy
 </li>
 </ul>
 
 
 
 
 You&rsquo;re not just supporting engineers &mdash; you&rsquo;re enabling an entire ecosystem.
 
 
 
 
 
 What you&rsquo;ll actually do
 
 <ul>
 <li>
 Build and scale Kubernetes-based platform infrastructure, keeping systems highly available under unpredictable load
 </li>
 <li>
 Drive system reliability, availability, and performance. Preventing resource contention between critical and heavy workloads
 </li>
 <li>
 Building secure, multi-tenant platforms without performance trade-offs
 </li>
 <li>
 Automate everything that shouldn&rsquo;t require human intervention
 </li>
 <li>
 Design solutions for high-throughput, data-heavy workloads. Running GPU-powered and compute-intensive workloads efficiently.
 </li>
 <li>
 Optimize scalability. Scaling storage and networking for massive data volumes. Designing smart autoscaling and cost-efficient infrastructure.
 </li>
 <li>
 Support and unblock internal teams using the platform
 </li>
 <li>
 Continuously improve observability, alerting, and incident response
 </li>
 <li>
 Be part of an on-call rotation where reliability truly matters
 </li>
 </ul>
 
 
 
 
 You:
 
 <ul>
 <li>
 Have solid experience in SRE, Platform, or Infrastructure Engineering for data platforms or big data tooling.
 </li>
 <li>
 Are comfortable with Kubernetes in production environments
 </li>
 <li>
 Understand distributed systems and failure modes
 </li>
 <li>
 Care deeply about reliability, observability, and automation
 </li>
 <li>
 Enjoy solving complex problems rather than following playbooks
 </li>
 <li>
 Exposure to high-performance workloads or GPU environments
 </li>
 </ul>
 
 
 
 
 
 Salary and benefits
 
 
 
 The base salary is between 90K and 150K euro, depending on what you bring to the table in terms of skills, personality and experience.
 
 
 Some of the secondary benefits: a permanent contract, 20% guaranteed bonus in stock, 10K+ personal allowance (can be used as extra salary), healthcare allowance (~2K a year), holiday allowance (8%), holidays, laptop, travel allowance, relocation package for Internationals, training &amp; conferences, 30% ruling, VISA sponsorship, luxury lunch, snacks/drinks, work from home setup, etc.
 
 
 
</div>

SRE - Data Platform Engineer - Kubernetes - top 0,5% biggest data set globally

Xccelerated | Part of Xebia

<div>
 
 Data Engineer
 
 
 
 
 
 
 Location:
 
 Netherlands (hybrid)
 
 
 
 Language:
 
 English-speaking environment
 
 
 
 
 
 Are you a
 
 Data Engineer based in Europe
 
 who wants to take the next step in your career?
 
 
 Would you like to
 
 relocate to the Netherlands
 
 and work on a modern cloud-based data platform while accelerating your growth through structured, high-level training?
 
 
 
 
 
 At
 
 Xccelerated
 
 , we are looking for
 
 Medior Data Engineers
 
 to help build a
 
 state-of-the-art data platform on Google Cloud Platform (GCP)
 
 . You&rsquo;ll work alongside experienced consultants and follow an intensive training program designed to rapidly strengthen your technical and professional skills.
 
 
 
 
 
 💡 Why join Xccelerated?
 
 <ul>
 <li>
 Build a
 
 modern data platform from scratch
 
 on Google Cloud Platform
 </li>
 <li>
 Work on real client projects while continuously improving your skills
 </li>
 <li>
 Receive
 
 60+ days of structured technical training
 
 led by experienced Tech Leads
 </li>
 <li>
 Collaborate with international engineering and analytics teams
 </li>
 <li>
 After one year, transition to a
 
 direct contract with the client organization
 
 </li>
 </ul>
 
 
 
 
 🚀 Your role as a Data Engineer
 
 
 As a Data Engineer at Xccelerated, you will help lay the foundation for data-driven decision making.
 
 
 You will:
 
 <ul>
 <li>
 Design, build and maintain
 
 scalable data pipelines
 
 on GCP
 </li>
 <li>
 Develop components of a modern data platform supporting analytics and machine learning use cases
 </li>
 <li>
 Ensure reliable integration between data sources, pipelines and data models
 </li>
 <li>
 Work closely with engineering, analytics and cloud teams on architecture and implementation
 </li>
 <li>
 Continuously improve data quality, reliability and performance
 </li>
 </ul>
 
 
 
 
 👩 💻 Who we&rsquo;re looking for
 
 
 You&rsquo;re a hands-on engineer who enjoys learning by doing and working on meaningful data challenges.
 
 
 You bring:
 
 <ul>
 <li>
 A Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data, Cloud or a related field
 </li>
 <li>
 
 3&ndash;4 years of experience
 
 as a Data Engineer (or similar role)
 </li>
 <li>
 Strong skills in
 
 Python
 
 and
 
 SQL
 
 </li>
 <li>
 Experience with at least one public cloud:
 
 GCP, AWS or Azure
 
 </li>
 <li>
 Familiarity with modern data tools such as
 
 Spark, Databricks or Airflow
 
 </li>
 <li>
 A proactive mindset and willingness to
 
 relocate to the Netherlands
 
 </li>
 </ul>
 
 
 
 
 🎁 What we offer
 
 
 At Xccelerated, we offer more than just a job &mdash; we offer a
 
 career accelerator in Data &amp; Cloud
 
 .
 
 <ul>
 <li>
 Competitive salary based on experience
 </li>
 <li>
 
 Relocation support
 
 for candidates moving to the Netherlands
 </li>
 <li>
 25 vacation days
 </li>
 <li>
 Intensive onboarding with a hands-on technical bootcamp
 </li>
 <li>
 Weekly technical training, mentoring and innovation days
 </li>
 <li>
 Company laptop and modern tooling
 </li>
 <li>
 Great lunches, excellent coffee and a well-stocked snack bar
 </li>
 <li>
 Hybrid working model and an international team
 </li>
 </ul>
 
 
 
 
 🌍 About Xccelerated
 
 
 Xccelerated (part of the
 
 Xebia Group
 
 ) is a tech accelerator focused on developing the next generation of Data &amp; Cloud professionals.
 
 
 You&rsquo;ll work
 
 four days per week
 
 on real client projects and spend
 
 one day per week
 
 on structured training, coaching and innovation. After one year, you&rsquo;ll have the opportunity to join one of our partner companies directly.
 
 
 
 
 
 📩 Interested in relocating to the Netherlands?
 
 
 We&rsquo;d love to hear from you.
 
 
 📧 Contact:
 
 Samantha Goncalves
 
 &ndash; sgoncalves@xccelerated.io
 
 
 📞 Phone: +31 6 13889770
 
 
 
 
 
 📋 Application process
 
 <ol>
 <li>
 Introductory interview with one of our recruiters
 </li>
 <li>
 Technical assessment &amp; presentation with one of our Tech Leads (remote)
 </li>
 </ol>
 
 We aim to complete the process within
 
 1&ndash;3 weeks
 
 , depending on availability.
 
</div>

Data Engineer (Relocation to the Netherlands)

Doghouse Recruitment

<div>
 
 
 Data Engineer &ndash; SaaS (Amsterdam / Hybrid / up to &euro;85K + bonus)
 
 
 
 
 
 
 My client is looking for a Data Engineer to help take their customer app to the next level. The app is used by over 700,000 users and relied on daily by thousands, playing a key role in enabling more sustainable travel by bike, car, and scooter. In this role, your work will directly influence how people move through their cities every day.
 
 
 
 
 
 You will join a passionate team of 20 engineers working with modern technologies, focused on building new features, improving performance, and delivering an outstanding user experience. On top of this, the company is preparing for a major data migration and expects to onboard an additional 100,000 users in the short term making scalability, reliability, and performance more critical than ever.
 
 
 
 
 
 
 The role
 
 
 
 You will be working on a modern data platform built with Clickhouse, Airflow, Python, Docker, and Kubernetes. The platform processes large volumes of operational and event data and delivers reliable analytics at scale.
 
 
 
 
 
 In this role, you will design, build, and maintain robust data pipelines, improve and optimise data models, and help further develop the overall data platform. You will work closely with product and engineering teams to ensure data is accurate, reliable, and scalable across the organization.
 
 
 You will join a strong engineering environment where modern tooling, ownership, and impact are key.
 
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 5+ years of experience as a Data Engineer or similar role
 </li>
 <li>
 Strong experience with Python and building data pipelines
 </li>
 <li>
 Hands-on experience with DBT, Snowflake, Airflow, or similar orchestration tools
 </li>
 <li>
 Experience with CI/CD, Docker &amp; Kubernetes.
 </li>
 </ul>
 
 
 
 
 
 Benefits
 
 
 <ul>
 <li>
 Salary up to &euro;85K (including holiday allowance)
 </li>
 <li>
 Hybrid working model (60/40)
 </li>
 <li>
 Start with a 12-month contract, with permanent hire thereafter
 </li>
 <li>
 &ldquo;Check-out day&rdquo; every two weeks for a mental reset
 </li>
 <li>
 Bonus scheme
 </li>
 <li>
 &euro;2,000 annual learning budget
 </li>
 <li>
 Ability to work remotely for 2 weeks per year (including workations)
 </li>
 </ul>
 
 
 
 
 
 Interested? Apply or contact me at glenn@doghouse-recruitment.nl
 
 
</div>

Data Engineer

Optics11

<div>We are expanding our team with a Data &amp; ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.<h4 class="jobSectionHeader">
Key Responsibilities:</h4><ul><li>
Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.</li><li>
Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.</li><li>
Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.</li><li>
Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.</li><li>
Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.</li><li>
Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.</li><li>
Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.</li><li>
Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.</li><li>
Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.</li><li>
Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.</li><li>
Manage resource and cost controls, including budgets, approvals, and tracking.</li><li>
Contribute to documentation, operational runbooks, and onboarding materials for the platform.</li></ul><h4 class="jobSectionHeader">
Why Join Us?</h4><ul><li>
Join one of Europe&rsquo;s most promising deep-tech scale-ups.</li><li>
Shape the future of a rapidly growing multidisciplinary R&amp;D organization.</li><li>
Competitive salary and benefits package.</li><li>
Enjoy regular team activities and company events.</li><li>
Fresh team lunches provided3 times/ week.</li></ul><h4 class="jobSectionHeader"> 
Required Qualifications:</h4><ul><li>
MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.</li><li>
Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).</li><li>
Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.</li><li>
Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).</li><li>
Experience with AWS Sagemaker or Google Vertex AI platforms.</li><li>
Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).</li><li>
Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).</li><li>
Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).</li><li>
Experience with CI/CD systems (e.g., GitLab CI).</li><li>
Programming skills in Python.</li><li>
Experience with monitoring and observability tools (Prometheus, Grafana).</li><li>
Understanding of DevOps and MLOps practices.</li><li>
Ability to work across teams and translate requirements into scalable infrastructure solutions.</li></ul>
Nice to Have:<ul><li>
Knowledge of data governance, IAM, and security frameworks.</li><li>
Experience with cost optimization and resource management in cloud environments.</li><li>
Familiarity with multi-tenant or multi-product platform architectures.</li><li>
Experience working with EU data compliance constraints in energy, maritime or defense.</li></ul>
Security &amp; Compliance:
You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.</div>

Data & ML Infrastructure Engineer

HCLTech

Amsterdam, Noord-Holland
 Job Summary
 
Need a secure access file transfer engineer with Devops knowlege
 
Key Responsibilities
 
Job Responsibilities : Design and operate the interactive access service based on Apache Guacamole, containerized on OpenShift, translating HTTPS to native protocols (SSH, RDP, SQL, VNC) Build and maintain the file transfer service using S3 presigned URLs with time-bound expiry and tenant-scoped bucket/path access controls Engineer tenant isolation within shared SaaS-style deployments, ensuring each consuming team only accesses their own targets through predefined connections and network-level controls Design credential lifecycle automation: retrieval/seeding at session start, reset/removal at session stop, integrated with Privileged Access Management and credential stores Build session recording and logging pipelines shipping audit data to tenant-specified log repositories (Kafka, S3, Git) Implement metering and billing event generation for per-tenant consumption tracking Develop CI/CD pipelines for automated platform lifecycle management: provisioning, start, stop, decommissioning (immutable infrastructure / cattle model) Collaborate with security teams to refine detection scenarios every sprint and maintain audited control reporting (Seven IT Risk Controls, COBIT framework) Define and monitor SLIs/SLOs: start latency, session success rate, recording completeness, tenant isolation violations, credential reset compliance
 
Skill Requirements
 
Skill Requirement : 3+ years hands-on experience with Kubernetes/OpenShift in production (deployment, networking, RBAC, persistent storage, operators) Proficiency in Go and Python for platform service development, automation, and tooling Strong Linux systems engineering (SSH, networking, security hardening, systemd) Container orchestration and CI/CD pipeline design (Helm, ArgoCD, Tekton, or equivalent) S3-compatible object storage (MinIO or AWS S3): presigned URLs, bucket policies, IAM integration Azure DevOps for backlog management, CI/CD pipelines, and release workflows Infrastructure as Code: Terraform, Ansible, or equivalent Understanding of security principles: zero-trust, defence-in-depth, protocol insulation, MFA, credential management Experience with IAM systems, directory services integration, and conditional access policies Familiarity with logging and monitoring stacks (Kafka, Elasticsearch, Prometheus/Grafana)
 
Other Requirements
 
Other Requirement : VMware Cloud Foundation (VCF) experience or familiarity with VCF-based infrastructure Experience with Privileged Access Management tooling (CyberArk, HashiCorp Vault) Experience with Apache Guacamole or similar remote access gateway technologies RDP and Windows Server administration knowledge Experience in regulated financial services environments (DORA, NIS2, or equivalent regulatory frameworks) Understanding of network segmentation and firewall rule management SOC2 Type 2 reporting or equivalent security assurance frameworks Experience designing metering/billing event pipelines for internal platform products
 
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SME - Microsoft Azure - IaaS, Azure Kubernetes Service

<div>
 We are expanding our team with a
 
 Data &amp; ML Infrastructure Engineer
 
 who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
 
 
 In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
 
 
 This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
 </li>
 <li>
 Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
 </li>
 <li>
 Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
 </li>
 <li>
 Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
 </li>
 <li>
 Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
 </li>
 <li>
 Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.
 </li>
 <li>
 Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.
 </li>
 <li>
 Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.
 </li>
 <li>
 Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.
 </li>
 <li>
 Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.
 </li>
 <li>
 Manage resource and cost controls, including budgets, approvals, and tracking.
 </li>
 <li>
 Contribute to documentation, operational runbooks, and onboarding materials for the platform.
 
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 <ul>
 <li>
 Join one of Europe&rsquo;s most promising deep-tech scale-ups.
 </li>
 <li>
 Shape the future of a rapidly growing multidisciplinary R&amp;D organization.
 </li>
 <li>
 Competitive salary and benefits package.
 </li>
 <li>
 Enjoy regular team activities and company events.
 </li>
 <li>
 Fresh team lunches provided3 times/ week.
 
 
 
 </li>
 </ul>
 Required Qualifications:
 
 
 <ul>
 <li>
 MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.
 </li>
 <li>
 Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).
 </li>
 <li>
 Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.
 </li>
 <li>
 Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).
 </li>
 <li>
 Experience with AWS Sagemaker or Google Vertex AI platforms.
 </li>
 <li>
 Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).
 </li>
 <li>
 Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).
 </li>
 <li>
 Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).
 </li>
 <li>
 Experience with CI/CD systems (e.g., GitLab CI).
 </li>
 <li>
 Programming skills in Python.
 </li>
 <li>
 Experience with monitoring and observability tools (Prometheus, Grafana).
 </li>
 <li>
 Understanding of DevOps and MLOps practices.
 </li>
 <li>
 Ability to work across teams and translate requirements into scalable infrastructure solutions.
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Knowledge of data governance, IAM, and security frameworks.
 </li>
 <li>
 Experience with cost optimization and resource management in cloud environments.
 </li>
 <li>
 Familiarity with multi-tenant or multi-product platform architectures.
 </li>
 <li>
 Experience working with EU data compliance constraints in energy, maritime or defense.
 
 
 
 </li>
 </ul>
 
 Security &amp; Compliance:
 
 
 
 You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.
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