Top Infrastructure Engineer Jobs in London

Design and maintain multi-region AWS infrastructure with Terraform, build isolated single-tenant environments, architect CI/CD (GitHub Actions, Docker), implement observability (OpenTelemetry, Datadog/Grafana), and tune autoscaling and networking for high-throughput LLM workloads to ensure reliability and low latency.

As a Corporate Infrastructure Engineer, you will design, develop, and implement corporate IT systems, managing AWS infrastructure and automation tools while improving IT systems.

Design, build, and operate foundational, multi-cloud platform systems. Own platform strategy, make build vs. buy decisions, write design docs and code, ensure reliability at scale, document decisions and run architectural forums, and improve operational maturity for enterprise-grade SaaS platform services.

Lead design and implementation of Astronomer’s multi-cloud platform infrastructure. Own platform strategy, build production-grade distributed systems (Kubernetes/Go), make architecture decisions, document designs and postmortems, and drive cross-team technical choices to scale a SaaS/PaaS offering for enterprise customers.

Design, build, and own Astronomer’s multi-cloud platform infrastructure. Lead architecture and implementation of production-scale distributed systems, operate Kubernetes clusters under load, write design docs and postmortems, make tradeoff decisions, and drive cross-team technology choices to improve platform scalability, reliability, and developer experience.

Maintain and improve reliability, scalability, and automation for user-facing production systems. Build infrastructure tooling, operate Kubernetes-based services, write IaC, participate in on-call and incident response, and advance observability and runbooks to reduce toil and improve platform reliability.

Manage, support and enhance global network infrastructure across on-premises, colocation and cloud. Design and build network products, maintain network security (next-gen firewalls), implement BAU changes, develop IaC and SRE-driven network automation, support cloud networking (Azure/AWS), run data centre operations and manage third-party vendors.

Lead deployment of North product in private cloud environments, partnering with clients to assess infrastructure needs and resolving technical issues effectively.

Design, build and maintain infrastructure tooling for deployment, observability, orchestration, disaster recovery and CI/CD to support scalable, highly available production systems and customer integrations.

Build and maintain production-ready infrastructure tooling and automation for deployment, observability, orchestration, backup/disaster recovery, and CI/CD integrations to support scalable, highly available services.

Build and maintain large-scale data pipelines and distributed systems to ingest, process, store, and serve petabytes of vehicle data for ML and AD/ADAS development. Collaborate with ML and CV engineers, design cloud-native solutions, and ensure reliability, observability, and automation across data platforms.

Manage and evolve Linux platforms for global trading, lead automation frameworks, ensure performance, and support build/release management.

Design and implement AI infrastructure for cross-border e-commerce: optimize GPU scheduling on Kubernetes or Ray, improve cluster high-availability and fault tolerance, build efficiency evaluation models, and introduce cutting-edge AI infra technologies.

Design, operate, and scale highly available distributed systems for telemetry ingestion. Automate reliability, participate in on-call incident management, evaluate resilience and DR, collaborate with app teams, and use AI tooling to reduce toil.

As a Software Engineer in Infrastructure Reliability, you'll design and build resilient systems, optimize performance, improve automation, and collaborate with teams to enhance infrastructure reliability.

Lead the development of AI/ML infrastructure for simulations, collaborating with teams on model scaling, system design, and mentoring junior engineers.

The Senior Software Engineer will build and maintain scalable backend systems, focusing on performance and reliability in handling structured and unstructured data using PostgreSQL and ClickHouse. Responsibilities include project ownership, tackling infrastructure challenges, and collaborating within a small team.

As a Senior Platform Engineer at Fresha, you will enhance platform architecture, developer experience, and efficiency by automating processes, collaborating with teams, and extending observability capabilities.

The Infrastructure Support Engineer provides technical support in a mixed environment, focusing on desktop and network setups while learning automation skills, particularly in Python.

Ensure availability, reliability and scalability of AWS-based services. Manage IaC, CI/CD for containerised and serverless apps, monitor with Prometheus/Grafana/CloudWatch, respond to incidents, drive cost optimisation and platform improvements.

The role involves owning and improving the design, deployment, and operation of OpenStack and Kubernetes environments, ensuring performance, scalability, and security of GPU workloads, while collaborating cross-functionally and driving automation using infrastructure-as-code practices.

Collect and collate progress and cost data from multi-discipline teams, update Primavera P6 schedules and cost reports, prepare monthly progress vs baseline reports, and support governance and assurance of project controls processes for infrastructure projects.

Provide project controls support on high-value London infrastructure projects: develop WBS and baselines, monitor progress and cost, perform earned value analysis, produce control reports, recommend performance improvements, and liaise with clients and cost/planning engineers.

Provide project controls support on high-value London infrastructure projects: develop WBS and baselines, establish progress and cost monitoring, produce control reports, perform performance analysis, and recommend actions to clients.

This role involves collecting data for model training, operating cloud infrastructure, and collaborating with scientists to enhance dataset quality and efficiency.