Senior Backend AI Platform Engineer
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Key skills for this role
About the Role
We are hiring a Senior Backend Engineer with deep experience in distributed systems, event-driven architecture, Kubernetes, and Azure to design, build, and run production-grade infrastructure for AI platforms.
Key Skills for This Role
Responsibilities
- Design, build, and run production grade infrastructure for AI platforms
- Work with event driven architecture, distributed systems, Kubernetes, and Azure
Requirements
- Deep experience in distributed systems, event driven architecture, Kubernetes, and Azure
- Demonstrate genuine depth in at least three of the listed technical areas (event driven architecture, distributed systems, backend architecture, cloud, data infrastructure, observability, platform security)
- Experience building internal developer tooling or platform abstractions
- Experience with service mesh technologies (Istio, Linkerd, or similar) in production
- Experience with patterns such as CQRS, event sourcing, sagas, or the outbox pattern in production
- Experience with caching layer design (Redis), search infrastructure (Elasticsearch, OpenSearch), or other specialised data stores at production scale
- Familiarity with infrastructure demands of AI workloads from a backend platform perspective
- Experience in a regulated or government adjacent environment
- Hands on experience with platform security practices
Full Job Posting
Hiring: Senior Backend Engineer AI (Platform)
- We’re looking for a backend expert with deep experience in distributed systems, event driven architecture, Kubernetes, and Azure to design, build, and run production grade infrastructure.
Preferred Qualifications
- Experience building internal developer tooling or platform abstractions that have been adopted and trusted by other engineering teams.
- Experience with service mesh technologies (Istio, Linkerd, or similar) in production, including mTLS, traffic management, and observability integration.
- Experience with patterns such as CQRS, event sourcing, sagas, or the outbox pattern in production — including the failure modes and operational complexity they introduce.
- Experience with caching layer design (Redis), search infrastructure (Elasticsearch, OpenSearch), or other specialised data stores at production scale.
- Familiarity with the infrastructure demands of AI workloads — retrieval pipelines, embedding stores, model serving layers — from a backend platform perspective.
- Experience in a regulated or government adjacent environment: data sovereignty, audit trails, or compliance frameworks.
- Hands on experience with platform security practices: secrets rotation, identity federation, and zero trust networking patterns.
Technical Depth Expectations
- Candidates will be expected to demonstrate genuine depth in at least three of the following areas: event driven architecture, distributed systems design, backend architecture, cloud, data infrastructure, observability and reliability engineering, platform security.
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