Senior Backend Platform Engineer AI
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Key skills for this role
About the Role
CloudJune Technologies is seeking a Senior Backend Platform Engineer with deep experience in distributed systems, event-driven architecture, Kubernetes, and Azure to design and run production-grade infrastructure.
Key Skills for This Role
Responsibilities
- Design, build, and run production grade infrastructure
- Build internal developer tooling or platform abstractions
- Work with AI workloads from a backend platform perspective
Requirements
- Deep experience in distributed systems, event driven architecture, Kubernetes, and Azure
- Experience building internal developer tooling or platform abstractions adopted by other engineering teams
- 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 (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
- 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
Full Job Posting
Job Overview
- Hiring: Senior Backend Platform Engineer AI (Platform) Abu Dhabi, UAE.
- Looking for a backend expert with deep experience in distributed systems, event driven architecture, Kubernetes, and Azure.
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 — event streaming design, message broker operations, schema management, consumer topology, delivery semantics, and failure modes.
- Distributed systems design — consistency models, distributed transactions, idempotency, back pressure, circuit breaking.
- Backend architecture — API design, service decomposition, inter service communication patterns.
- Cloud — architecture, networking, compute, storage, and cost management at the depth required to design and operate production systems on Azure or equivalent.
- Data infrastructure — pipeline design, PostgreSQL at scale, polyglot persistence.
- Observability and reliability engineering — SLO design, instrumentation, distributed tracing, alerting strategy.
- Platform security — secrets management, network segmentation, identity and access management.
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