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Mid-senior metro platform role with broad cloud and distributed-systems requirements increases applicant density moderately.
Core platform and distributed-systems skills transfer across industries, though industrial contextualisation gives an advantage.
Explicit 6–8 years plus mandatory JVM/Python, cloud, orchestration, and observability skills creates strict filters.
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Design, build, and operate the core serverless execution engine and workflows orchestration layer for Cognite Data Fusion’s AI and automation platform.
Own uptime, latency SLOs, incident response, and reliability engineering for platform services ensuring deterministic function execution and workflow progress without data loss.
Architect and implement scalable, multi-tenant, multi-cloud high-throughput workload scheduling, queuing, retry mechanisms, API design, observability, and CI/CD pipeline solutions.
6–8 years of experience building and operating production backend services at scale.
Proficiency in JVM languages (Kotlin preferred, Java acceptable), Python (FastAPI), distributed systems patterns, and cloud-native service design (Kubernetes, Azure, GCP, AWS).
Hands-on experience with workflow engines (e.g., Conductor, Apache Airflow) and event-driven architectures (Kafka, Pub/Sub).
Work Experience Required: 6–8 years of relevant backend software engineering experience.
Experienced in designing platform systems enabling ML and automation workflows, emphasizing composability, documentation, and automation.
Skilled in working with observability tools such as Open-telemetry, Prometheus, and Grafana to ensure operational insight and uptime.
Familiarity with industrial domain contextualization, ML workload support, and multi-tenant data storage and caching layers for high-throughput industrial workloads.