Principal Platform Engineer (Python)
Location: Remote from Spain (an indefinite Spanish employment contract)
Our client is a leading global 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.
Agentic Security and AI-Ready Data Foundations.
We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.
This is an * engineering * role, not an analytical one. You will build the agentic workflows that reason over the firm's research content, and the ingestion, evaluation and guardrail tooling that makes their output trustworthy enough for investment professionals to act on. None of this tooling exists today — you would be building it from scratch.
7+ years of software engineering experience, a substantial part of it spent building infrastructure or platform tooling rather than operating it
~ Python (required): production-level Python, including packaging, typing, testing, and dependency management. The platform is written in Python and you will be extending it, not scripting around it. Bash for glue work.
~ On-premises infrastructure: solid Linux fundamentals, and working knowledge of the networking, storage, and virtualization layers the platform sits on. Infrastructure as code: hands-on experience with Terraform and Ansible, which are the tools in use here. Infrastructure here lives in version control and goes through review like application code.
~ Docker and Kubernetes, including how workloads are scheduled, configured, and given access to resources at runtime.
~ CI/CD and DevOps practice: experience building pipelines (GitLab CI, GitHub Actions, Jenkins) that test, package, and release software, and the practices around them, such as automated testing and staged rollouts.
~ Observability: experience instrumenting systems with Prometheus, Grafana, OpenTelemetry, or an equivalent stack, and using that data to diagnose failures in distributed systems.
~ Security (critical): a working grasp of secrets management, identity and access control, network isolation, supply-chain and dependency risk, and how to build these into a platform so that consuming teams inherit them by default.
~ Collaboration and communication: you can gather requirements from engineering teams and write documentation others can work from. You will also need to explain design decisions to people outside engineering.
~ English level B2
Adaptability: comfortable when priorities, tools, or requirements change mid-project.
Communication: you write and speak clearly enough that other teams can act on it without a follow-up meeting.
Ownership: you follow a problem to its cause rather than handing it on when the symptom clears.
Attention to detail: you notice the small things, like an unpinned dependency or a permission wider than it needs to be.
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.
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.
This role sits at the intersection of data engineering, AI, and financial services, solving one of the most important challenges in enterprise AI: enabling agents to securely access and reason over trusted data. You'll have the opportunity to design and build foundational platforms that combine large-scale data systems, governance, and AI technologies in highly regulated environments. It offers significant technical ownership, exposure to cutting-edge AI agent architectures, and the chance to shape how organisations safely unlock value from their data.
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Location: Remote from Spain (an indefinite Spanish employment contract)
Our client is a leading global 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.
Agentic Security and AI-Ready Data Foundations.
We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.
This is an * engineering * role, not an analytical one. You will build the agentic workflows that reason over the firm's research content, and the ingestion, evaluation and guardrail tooling that makes their output trustworthy enough for investment professionals to act on. None of this tooling exists today — you would be building it from scratch.
7+ years of software engineering experience, a substantial part of it spent building infrastructure or platform tooling rather than operating it
~ Python (required): production-level Python, including packaging, typing, testing, and dependency management. The platform is written in Python and you will be extending it, not scripting around it. Bash for glue work.
~ On-premises infrastructure: solid Linux fundamentals, and working knowledge of the networking, storage, and virtualization layers the platform sits on. Infrastructure as code: hands-on experience with Terraform and Ansible, which are the tools in use here. Infrastructure here lives in version control and goes through review like application code.
~ Docker and Kubernetes, including how workloads are scheduled, configured, and given access to resources at runtime.
~ CI/CD and DevOps practice: experience building pipelines (GitLab CI, GitHub Actions, Jenkins) that test, package, and release software, and the practices around them, such as automated testing and staged rollouts.
~ Observability: experience instrumenting systems with Prometheus, Grafana, OpenTelemetry, or an equivalent stack, and using that data to diagnose failures in distributed systems.
~ Security (critical): a working grasp of secrets management, identity and access control, network isolation, supply-chain and dependency risk, and how to build these into a platform so that consuming teams inherit them by default.
~ Collaboration and communication: you can gather requirements from engineering teams and write documentation others can work from. You will also need to explain design decisions to people outside engineering.
~ English level B2
Adaptability: comfortable when priorities, tools, or requirements change mid-project.
Communication: you write and speak clearly enough that other teams can act on it without a follow-up meeting.
Ownership: you follow a problem to its cause rather than handing it on when the symptom clears.
Attention to detail: you notice the small things, like an unpinned dependency or a permission wider than it needs to be.
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.
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.
This role sits at the intersection of data engineering, AI, and financial services, solving one of the most important challenges in enterprise AI: enabling agents to securely access and reason over trusted data. You'll have the opportunity to design and build foundational platforms that combine large-scale data systems, governance, and AI technologies in highly regulated environments. It offers significant technical ownership, exposure to cutting-edge AI agent architectures, and the chance to shape how organisations safely unlock value from their data.
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