About the Role:nnWe’re building a large-scale synthetic data generation engine to produce realistic observability datasets — metrics, logs, and traces — to support AI/ML training and benchmarking. You will design, implement, and scale pipelines that simulate complex production environments and emit controllable, parameterized telemetry data.nResponsibilitiesnt•tDesign and implement generators for metrics (CPU, latency, throughput) and logs (structured/unstructured).nt•tBuild configurable pipelines to control data rate, shape, and anomaly injection.nt•tDevelop reproducible workload simulations and system behaviors (microservices, failures, recoveries).nt•tIntegrate synthetic data storage with Prometheus, ClickHouse, or Elasticsearch.nt•tCollaborate with ML researchers to evaluate realism and coverage of generated datasets.nt•tOptimize for scale and reproducibility using Docker containers.nQualificationsnt•tStrong programming skills in Python.nt•tFamiliarity with observability tools (Grafana, Prometheus, ELK, OpenTelemetry).nt•tSolid understanding of distributed systems metrics and log structures.nt•tExperience building data pipelines or synthetic data generators.nt•t(Bonus) Knowledge of anomaly detection, time-series analysis, or generative ML models.nPayn – $50 – 75/hr depending on experiencen – Remote, flexible hours