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Manish Ravikanti
Mid-level Site Reliability Engineer specializing in cloud infrastructure and Kubernetes
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Backend/infra-focused engineer who owned production systems for distributed ML experimentation (hyperparameter tuning across a cluster with GPU scaling, custom scheduling, and checkpoint-based fault tolerance). Also built and operated a low-latency log validation service using queued async workflows with idempotency, retries/backoff, and strong observability, plus experience building resilient Selenium-based browser automations for complex multi-step web flows.
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Key Strengths
- Built and owned an end-to-end distributed hyperparameter tuning platform with orchestration, scaling, and checkpointing
- Designed custom cluster scheduling to reduce resource imbalance and improve throughput
- Implemented fault-tolerant checkpoint/resume for long-running distributed training jobs
- Built low-latency backend processing using queues/workers to absorb traffic spikes
- Production reliability practices: retries with exponential backoff, idempotency/deduplication, stateless services, and job reprocessing on worker failure
- Strong observability implementation (structured logging, metrics, dashboards, alerting)
- API design for internal/external consumers with versioning, documentation, and token-based auth
- Browser automation robustness: stable selectors, explicit waits, Page Object Model, and handling flaky UI/network states
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