Senior Platform Engineer (Python)

Intellias; Slough; On-site; GBP 70,000 - 110,000; Full time

Job summary

Intellias is seeking a senior infrastructure platform engineer in the UK to advance AI adoption across on-prem environments. You will collaborate with engineering teams to turn pain points into scalable platform features, automate operations, and build self-service workflows.

The role emphasizes strong Python, Linux, Terraform/Ansible, Docker and Kubernetes proficiency, plus observability with Prometheus, Grafana and OpenTelemetry.

Qualifications

• 5+ years of software engineering with a focus on infrastructure
• Strong production-level Python with testing, typing and packaging
• Solid Linux fundamentals and on-prem networking/storage/virtualization
• Hands-on Terraform and Ansible experience
• Docker and Kubernetes knowledge with workload scheduling and runtime access
• Experience building CI/CD pipelines and observability tooling
• Security-minded with secrets, IAM and dependency hygiene
• Strong troubleshooting and cross-team communication

Responsibilities

• Build and scale an internal infrastructure platform
• Work with engineering to convert recurring pain points into platform features
• Automate manual infrastructure operations and build self-service workflows
• Implement observability through metrics, logging and alerting
• Own production issues from triage to permanent resolution
• Manage on-prem compute and storage with cost and performance focus
• Embed security best practices and contribute to the platform roadmap

Skills

Python

Linux fundamentals

Terraform

Ansible

Docker

Kubernetes

CI/CD pipelines

Observability

Security fundamentals

Troubleshooting

Communication

Tools

Terraform

Ansible

Docker

Kubernetes

Prometheus

Grafana

OpenTelemetry

GitLab CI

GitHub Actions

Jenkins

Job description

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.

Requirements

• 5+ years of software engineering experience, with a strong focus on infrastructure or platform engineering.
• Strong production-level Python, including testing, typing, packaging, and dependency management; Bash for scripting.
• Solid Linux fundamentals and understanding of on-prem infrastructure, including networking, storage, and virtualization.
• Hands-on experience with Terraform and Ansible.
• Strong Docker and Kubernetes knowledge, including workload scheduling, configuration, and runtime resource access.
• Experience building CI/CD pipelines with GitLab CI, GitHub Actions, Jenkins, or similar tools.
• Experience with observability tools such as Prometheus, Grafana, and OpenTelemetry.
• Good understanding of platform security: secrets management, IAM, network isolation, and dependency/supply-chain risks.
• Strong troubleshooting skills and experience diagnosing complex distributed system issues.
• Good communication and documentation skills; ability to collaborate with engineering and non-technical stakeholders.
• English B2+.

Personal Skills

• Adaptability and strong problem-solving mindset.
• Ownership and attention to detail.
• Clear communication and effective cross-team collaboration.
• Ability to prioritize competing requests.
• User-focused approach to building internal platforms.

Responsibilities:

internal infrastructure platform.

Work with engineering teams to identify recurring pain points and turn them into scalable platform features.

Automate manual infrastructure operations and build reliable self-service workflows.

Implement observability through metrics, structured logging, and alerting.

Own platform production issues from troubleshooting and root cause analysis to permanent resolution.

Manage and scale on-prem compute and storage infrastructure with performance and cost in mind.

Embed security best practices, including secrets management, least-privilege access, dependency hygiene, and auditability.

Evaluate new tools and technologies and contribute to the platform roadmap.