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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level backend platform role, metro location, and generic "Software Engineer" title increase applicant competition.
Platform and backend skills transfer across industries but industrial domain and ML-platform experience increase domain specificity.
Explicit 3–6 years plus specific JVM/Python, cloud, Kubernetes, workflows, and observability requirements raise screening strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and operate core serverless execution engine and workflows orchestration layer driving AI and automation capabilities.
Own uptime, latency SLOs, incident response, and observability for platform services to ensure reliable and deterministic execution.
Architect scalable multi-tenant, multi-cloud service with APIs, scheduling, retry logic, and CI/CD pipelines ensuring high throughput and performance.
Minimum Requirements
3–6 years of experience building and operating production backend services at scale.
Proficiency in JVM languages (Kotlin preferred or Java) and Python (FastAPI).
Experience with distributed systems, cloud-native service design (Kubernetes, Azure, GCP, AWS), workflow engines (Conductor, Apache Airflow, or equivalent), and event-driven architectures (Kafka, Pub/Sub).
Work Experience Required: 3–6 years as specified
Ideal Candidate Profile
Strong platform thinking with ability to build composable, well-documented, automated systems that enable other engineers, including ML engineers, to build faster.
Experience supporting ML workloads in production including job scheduling, resource management, and model serving infrastructure.
Familiarity with observability stacks (OpenTelemetry, Prometheus, Grafana) and multi-tenant data storage and caching solutions (PostgreSQL, Redis, object storage).