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Yash Tobre
Mid-level AI/ML Engineer specializing in computer vision, NLP/LLMs, and MLOps
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About
ML/AI engineer with defense and commercial analytics experience: deployed a real-time aerial object detection system at Dynetics (YOLOv5 + TorchServe in Docker on AWS EC2) with drift-triggered retraining and 99.5% uptime, tackling ambiguous targets and weather degradation. Previously at Fractal Analytics, built and explained a churn prediction model for marketing stakeholders using SHAP and delivered it via a Flask API into dashboards, driving a reported 22% attrition reduction.
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Key Strengths
- Built and deployed real-time aerial object detection pipeline to AWS with 99.5% uptime under strict SLA
- Reduced false positives via targeted negative sampling and sensor-informed post-processing (velocity/flight profile)
- Improved robustness to adverse weather using simulated-weather augmentation and data-quality gating
- Designed self-healing MLOps workflows: drift checks, automated retraining, and controlled zero-downtime rollouts with Airflow + MLflow
- Production evaluation discipline: unit/integration tests, shadow mode validation, and A/B testing tied to KPIs
- Strong stakeholder translation: explained churn drivers with SHAP and delivered actionable dashboard integration; achieved 22% churn reduction
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