Principal Systems Engineer - IaaS/PaaS

Skills

About the Role

You will design and evolve core compute, storage, and orchestration systems for large-scale data and ML workloads. You will implement secure, reliable, and scalable Kubernetes platforms, including Operators, CI/CD pipelines, and IAM. You will collaborate with ML, product, and infrastructure teams to enable efficient data pipelines, feature stores, and training workflows across heterogeneous hardware. You will maintain platform reliability through observability, automation, and proactive performance optimization. You will define standards for deployment, validation, and operational readiness, and lead vendor evaluation and integration for key technologies.

Requirements

• 10+ years of software, systems, or data engineering experience and 3+ years in technical leadership or management.
• Expertise in distributed systems, data streaming with Kafka, Flink, and Spark, and ML orchestration with Airflow, Kubeflow, and MLflow.
• Proficiency in Go and Python; hands-on experience with Kubernetes, Docker, Terraform, and Ansible.
• Experience with observability stacks including Prometheus, Grafana, ELK, and FluentBit, plus platform security.
• Experience delivering data migrations, hybrid cloud architectures, and large-scale CI/CD automation.
• Familiarity with Snowflake, Iceberg, Delta Lake, and vector databases including PgVector, Milvus, and LanceDB.
• Track record of successful client delivery across cloud, media, industrial ML, or hardware integration.
• Excellent communication, cross-team collaboration, and mentoring skills.
• Preferred background in HPC, ML infrastructure, or sovereign and regulated environments.
• Preferred familiarity with energy-aware computing, modular data centers, or ESG-driven infrastructure design.
• Preferred experience collaborating with European and global engineering partners.

Responsibilities

• Design and evolve core compute, storage, and orchestration systems for large-scale data and ML workloads.
• Implement secure, reliable, and scalable Kubernetes platforms, including Operators, CI/CD pipelines, and IAM systems.
• Collaborate with ML, product, and infrastructure teams to enable efficient data pipelines, feature stores, and training workflows on heterogeneous hardware.
• Maintain platform reliability through observability, automation, and proactive performance optimization.
• Define standards for deployment, validation, and operational readiness across environments.
• Lead vendor evaluation and integration for technologies such as Kafka, Snowflake, MLflow, and Trino.
• Foster open-source contribution, innovation, and continuous learning.