Back
Mid-level platform role, metro location, broad skills and popular title increase candidate competition.
Platform engineering skills transfer across industries, though industrial AI and ML platform context raises domain specificity.
Explicit 6–8 years requirement plus mandatory JVM/cloud/workflow stack makes screening strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and operate the core serverless execution engine and workflows orchestration layer supporting AI and automation capabilities.
Own platform service uptime, latency SLOs, incident response, and ensure deterministic execution and failure-free workflows.
Architect for multi-tenant, multi-cloud, high-throughput systems with API-first design and observability tooling (Open-telemetry, Prometheus, Grafana).
6–8 years of engineering experience building and operating backend services at scale.
Proficiency in JVM languages (Kotlin preferred or Java) and Python (FastAPI), and experience with distributed systems design and cloud-native services (Kubernetes, Azure, GCP, AWS).
Experience with workflow engines (e.g., Conductor, Apache Airflow), event-driven architectures (Kafka, Pub/Sub), and relational/non-relational databases.
Experience with observability tools (Open-telemetry, Prometheus, Grafana) and containerized CI/CD pipelines.
Experienced in platform thinking with a focus on composable, automated systems that empower software and ML engineers to develop rapidly.
Familiarity with ML platform infrastructure supporting model training, experiment tracking, and batch inference in production.
Background or interest in industrial data contextualisation domains (knowledge graphs, entity resolution, NLP/CV pipelines) and multi-tenant data infrastructure.