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Senior principal role with niche workflow/platform skills but metro location yields medium competition.
Core backend and platform skills transfer across industries, though industrial contextualisation raises medium sensitivity.
Explicit 10+ years plus required JVM, workflow engines, cloud, and observability stack creates high shortlisting strictness.
Design, build, and operate the core serverless execution engine and workflow orchestration layer foundational to AI and automation capabilities.
Ensure platform reliability by owning uptime, latency SLOs, incident response, and preventing data loss or silent failures.
Architect for multi-tenant, multi-cloud high-throughput workloads with robust scheduling, queueing, retry mechanisms, and maintain API-first architecture with strong observability and CI/CD pipelines.
10+ years of engineering experience building and operating production backend services at scale.
Expertise in JVM languages (Kotlin preferred or Java), Python (FastAPI), distributed systems, and cloud-native service design (Kubernetes, Azure, GCP, AWS, Private cloud).
Hands-on experience with workflow engines (Conductor, Apache Airflow or equivalent) and event-driven architectures (Kafka, Pub/Sub).
Experience with relational (PostgreSQL) and non-relational databases, object storage, caching layers, and observability tools (Open-telemetry, Prometheus, Grafana).
Operates with a platform thinking mindset to build composable, well-documented, automated systems that empower engineers, including ML engineers.
Experienced in supporting ML platform workloads and workflows, including job scheduling, resource management, and experiment tracking integration.
Comfortable working in fast-paced startup-like environments solving complex data infrastructure problems for Fortune 500 industrial clients.