Skill labels: DevOps Practices, On-premises, Python, Security
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.
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.
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.
Requirements:
- 7+ 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.
Personal Skills
- Adaptability: comfortable when priorities, tooling, or migration scope shifts mid-project.
- Curiosity: pays attention to how other teams solve infrastructure problems and brings useful patterns back.
- Communication: writes and speaks clearly enough that other teams can act without a follow-up meeting.
- Teamwork: works well embedded with teams outside their own, and shares knowledge freely.
- Ownership: follows a problem to its root cause instead of handing it off once symptoms disappear.
- Attention to detail: catches the small things — an unpinned dependency, an overly broad permission, a config drift.
- Prioritization: can judge what to tackle first when multiple teams need help at the same time.
- User focus: treats the engineering teams using the platform as customers, and measures success by whether their work gets easier.
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.
- Evaluating new tools, libraries, and patterns, and folding the ones that earn their place into the platform roadmap.
Why this position:
- 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.