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Meghana Nandivada
Junior Machine Learning Engineer specializing in production ML systems and MLOps
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About
ML/AI engineer (TCS) who built and productionized a customer segmentation and personalized-offer recommendation pipeline end-to-end (data cleaning/feature engineering/clustering through Flask API deployment in Docker with monitoring). Emphasizes reliability and operational rigor via validation checks, periodic retraining, model/API versioning, and latency optimization, and has experience translating marketing KPIs into usable dashboards for non-technical teams.
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Key Strengths
- Built and deployed end-to-end customer segmentation & recommendation system to production
- Production ML engineering: data validation, retraining strategy, and model/API versioning for stability
- Performance optimization for low-latency inference (preprocessing + lightweight embeddings)
- Orchestrated ML/ETL workflows with Airflow; pragmatic framework selection vs lightweight orchestration
- Structured approach to AI agent/workflow reliability (unit + scenario tests, guardrails, benchmarking, stress tests, monitoring)
- Strong cross-functional delivery with non-technical marketing stakeholders (KPI translation, dashboards, iterative demos)
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