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Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
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About
ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.
Experience
Education
Key Strengths
- Built and deployed production ML monitoring/validation framework for drift and performance degradation
- Scaled high-volume real-time inference workloads with horizontal scaling and compute/memory optimization
- Designed automated anomaly detection and retraining triggers to maintain model reliability over time
- Hands-on orchestration of ML workflows using Apache Airflow (DAGs) and Kubernetes (container orchestration)
- Metrics-driven evaluation of AI workflows (accuracy, latency, reliability) with stress testing and production monitoring
- Able to translate non-technical stakeholder goals into ML pipelines and dashboards for decision-making
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