Senior Backend AI Platform Engineer

🚀 Hiring: Senior Backend Engineer-AI (Platform)-Abu Dhabi, UAE.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 patternsTechnical Depth Expectations Candidates will be expected to demonstrate genuine depth in at least three of following areas. Conceptual familiarity is not sufficient. Event-driven architecture — event streaming design, message broker operations, schema management, consumer topology, delivery semantics, and the failure modes of asynchronous systems in production. Distributed systems design — consistency models, distributed transactions, idempotency, back-pressure, circuit breaking, and the architectural decisions that determine how a system behaves when things go wrong. Backend architecture — API design, service decomposition, inter-service communication patterns, and the structural decisions that keep a multi-service surface area coherent and evolvable. 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, and the engineering discipline to keep data infrastructure trustworthy across multiple consumers. Observability and reliability engineering - SLO design, instrumentation, distributed tracing, alerting strategy, and the practices that turn operational data into systemic improvementsPlatform security - secrets management, network segmentation, identity and access management, and the practices that keep production infrastructure hardened over time. Apply at sahithi.reddy@cloudjune.com