Summary
✨ AI‑Generated
A growing technology organization is seeking a hands-on Senior Platform Engineer to scale cloud infrastructure and support increasing workloads. The role combines platform engineering and software development, requiring expertise in container orchestration, infrastructure automation, deployment pipelines, and reliable production systems.
Highlights
Fully remote senior engineering role with strong compensation, ownership of cloud platforms, opportunities to improve reliability, and a balance of infrastructure and software development work.
Description
Senior Platform Engineer (AI Native) - Fully Remote - UK/Europe - Circa £70,000 in the UK / equivalent European package - strong overlap with UK working hours required
We are working with a scaling technology business looking for a hands-on Senior Platform Engineer to help develop and scale its cloud platform as both customer traffic and AI workloads continue to grow.
This is a hybrid software engineering and platform/infrastructure role, with approximately 60% focused on infrastructure and 40% on software engineering.
The ideal person will have started their career within software engineering before moving into platform or infrastructure.
You will still be comfortable writing application code, while taking ownership across Kubernetes, cloud infrastructure, Terraform, CI/CD and production reliability.
This isn't a traditional reactive infrastructure or support role.
You will be expected to proactively identify problems, improve the platform and take ownership of complex engineering projects from design through to delivery.
Key Responsibilities
Design, build and improve scalable cloud infrastructure, primarily within GCP.Own and manage production Kubernetes environments, including troubleshooting complex cluster and application issues.Build and maintain infrastructure as code using Terraform.Design and improve CI/CD pipelines and engineering workflows.Contribute to application and platform code across Python, TypeScript and Java environments.Work with event-driven architecture and technologies such as Kafka/Confluent.Improve logging, monitoring and observability across the platform.Identify performance, reliability and scalability issues before they become production incidents.Support the infrastructure required for growing AI workloads, including AI gateways and model deployment.Contribute to internal AI tooling, automation and agentic workflows.Work across networking, security, IAM, secrets management and wider platform governance.Lead technical migrations and infrastructure projects from initial design through to implementation.Work closely with software engineers and wider product teams rather than operating within an isolated infrastructure function.Mentor other engineers through pairing, code reviews and knowledge sharing.
Essential Skills & Experience
Strong background in software engineering, with experience developing production applications before or alongside moving into platform/infrastructure engineering.Strong hands-on experience managing Kubernetes in production.Production cloud infrastructure experience, ideally within GCP, although strong AWS or Azure backgrounds will also be considered.Strong Terraform / Infrastructure as Code experience, including modules, state and infrastructure standards.Experience designing and building CI/CD pipelines.Software development experience using Python, TypeScript and/or Java.Good understanding of networking, security and cloud infrastructure best practices.Experience taking ownership of complex production infrastructure and resolving difficult technical problems.Comfortable moving between infrastructure and application code rather than working within a narrow technical specialism.Proactive approach to platform engineering, identifying opportunities to improve performance, reliability and developer experience.Comfortable using modern AI-assisted development tools as part of day-to-day engineering.
Desirable Experience
GCP-native infrastructure and services.Kafka / Confluent or other event-streaming technologies.Elasticsearch.OpenTelemetry and modern observability tooling.GitHub Actions.PostgreSQL or similar production databases.LLM APIs and AI gateways.MCP servers and agentic workflows.AI observability or evaluation tooling such as Langfuse or Phoenix.AI security, including testing AI applications and understanding potential attack surfaces.Workflow automation/orchestration tools such as n8n.Experience working within a startup, scale-up, fintech or B2B SaaS environment.