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Sharan Raj Sivakumar
Senior Software Developer specializing in AI/ML automation and cloud-native systems
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About
ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.
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Key Strengths
- Reduced manual labeling effort by 90% via automated label generation using iterative clustering
- Built multi-label Random Forest system for network performance issue detection and RCA acceleration
- Iteratively improved clustering quality by removing anomalous data using 99th percentile Wasserstein distance with domain validation
- Led development of an auto-deployment/orchestration platform at Ericsson
- Automated 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment
- Strong MLOps rigor: SLAs/metrics, blue-green deployments, regression testing, monitoring and drift detection
- Practical model selection and retrieval strategy design (vector DB/RAG, APIs, prompting patterns)
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