Our client is a leading integral investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.

Se pueden requerir diversas habilidades interpersonales y experiencia para el siguiente puesto. Por favor, asegúrese de consultar la descripción a continuación con atención.

As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.

Requirements:

- 5+ years of experience as a DevOps / platform / systems engineer, with a track record spanning both infrastructure operations and broader platform engineering — not purely a helpdesk/ops role.
- Software development background: comfortable reading and writing production code, understanding how applications are configured and run, not just how they're deployed. This helps when working directly alongside dev teams on their workloads.
- Python (required): solid, production-level scripting and tooling ability — automation, integrations, and support for platform/migration work. Not necessarily building a Python platform product, but extending and maintaining real code.
- Linux systems (required): strong fundamentals — processes, networking, storage, permissions, package management.





Comfortable troubleshooting at the OS level across a fleet of machines.

- Infrastructure as code: hands-on production experience with Ansible and Terraform. Able to write, review, and maintain IaC that's treated like application code (version control, review, testing).
- Migration experience (highly relevant): direct experience with, or strong aptitude for, OS-level migrations (e.G., CentOS to Ubuntu) and application/workflow migrations (e.G., Airflow 1.X DAGs to Airflow 2.X). Comfortable auditing existing workloads, identifying compatibility gaps, and planning cutover.
- Kubernetes: working knowledge of how clusters are built, upgraded, and retired, and how workloads get moved between clusters with minimal disruption — relevant to migrating teams off unsupported clusters onto supported ones.
- CI/CD: experience with at least one major pipeline tool (GitLab CI, GitHub Actions, Jenkins) and the practices around it — automated testing, staged rollouts, safe release processes.
- Observability: familiarity with Prometheus, Grafana, or equivalent, and the ability to use monitoring data to diagnose issues across distributed systems.
- Security awareness: practical understanding of secrets management, access control, and dependency/supply-chain hygiene — enough to spot and flag risk,



even if not the platform's primary security owner.
- Collaboration and communication: able to sit with a dev team, understand their workload and pain points, and communicate technical trade-offs clearly to both engineers and non-engineers.
- Problem-solving: persistence in root-causing failures rather than just restarting services or working around symptoms.

Responsibilities:
- Designing, building, and maintaining the Python services, libraries, and command-line tooling that make up the internal infrastructure platform.
- Working with the engineering teams who consume the platform to understand their workflows and turn recurring pain points into platform features.
- Replacing manual infrastructure operations with codified, self-service workflows, so that environments are reproducible, reviewable, and safe to change.
- Building observability into the platform through metrics, structured logging, and alerting, so failures surface before consuming teams report them.
- Owning production issues in the platform end to end, from triage and root cause analysis through the code or configuration change that prevents a recurrence.
- Managing and scaling the compute and storage the platform provisions across on-premises hardware, balancing performance against cost as usage grows.
- Building security into the platform by default: secrets management, least-privilege access, dependency hygiene, and an auditable history of every infrastructure change. xugodme
- Evaluating new tools, libraries, and patterns, and folding the ones that earn their place into the platform roadmap.

📌 Senior Platform Engineer (España)
🏢 Intellias
📍 España