Platform engineer in national security and AI safety

Описание:

Faculty develops and deploys responsible, human-centric AI for global customers across government, finance, retail, energy, life sciences, and defence. Its National Security and AI Safety business unit supports responsible AI development and deployment for national security, global stability, AI safety, and high-impact missions.

Design and build cloud platform infrastructure across multi-environment setups, including ownership of security controls;
Create and manage scalable Infrastructure as Code (IaC) for complex client environments;
Optimise container orchestration via Kubernetes to ensure high availability and resource efficiency;
Implement advanced monitoring, logging, and alerting frameworks to maintain production system health;
Manage production environments and own incident response and root cause analysis;
Collaborate with Software and ML engineers to ensure applications are architected for production readiness;
Mentor and coach Engineers through peer reviews and the sharing of engineering best practices.

Maintain high environment stability and drive infrastructure projects with minimal supervision, taking full accountability for system uptime;
Apply deep experience in cloud networking, security, and identity management (IAM) to harden infrastructure against threats;
Use Terraform or Cloud Formation alongside hands-on coding proficiency to deliver consistent, repeatable infrastructure;
Manage Kubernetes clusters and Docker environments at scale for modern ML platforms;
Use strong Python skills to build robust platform tooling that allows agentic systems to interact reliably with infrastructure;
Translate complex requirements into clear solutions and communicate technical trade-offs to engineering teams and client stakeholders;
Be eligible for UK Developed Vetting (DV), if required for government client work.

Candidates may need to work on site with government clients from time to time;
Part-time hours are open for discussion;
The interview process includes a Talent Team Screen, Pair Programming Interview, System Design Interview, and Commercial Interview;
Candidates may opt out of the AI note-taker used in interviews.