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Sri Lekha Kandadai

Mid-level Machine Learning Engineer specializing in MLOps, monitoring, and multimodal AI

AppleUniversity of Central MissouriKansas, USA4 Years ExperienceMid LevelWorks On-Site

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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.

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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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Experience

ML EngineerApple · Jul 2025 – Present
Ai EngineerCloud Redux · Jan 2022 – Jun 2023
ML EngineerIBM · May 2020 – Dec 2021

Education

University of Central Missourimaster, Computer Science (2025)
Vignana Bharathi Institute of Technologybachelor, Computer Science (2021)

Languages

English

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Sri Lekha KandadaiMid-level Machine Learning Engineer specializing in MLOps, monitoring, and multimodal AI