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Meghanath kethireddy
Mid-level Full-Stack/Backend Engineer specializing in Java microservices and cloud platforms
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About
PayPal ML/AI practitioner who built and productionized a hybrid recommendation engine (BERT/LLM embeddings + collaborative filtering + XGBoost ranking) on AWS with end-to-end MLOps and orchestration. Addressed real-world issues like cold start and embedding latency (ONNX, clustering, caching, PySpark/Delta Lake) and drove a 27% lift in upsell conversion via A/B testing and stakeholder collaboration with marketing.
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Key Strengths
- Built and deployed a hybrid recommendation engine (BERT + collaborative filtering + XGBoost ranking) to production on AWS
- Delivered measurable business impact: increased PayPal upsell conversion by 27%
- Solved key production challenges: embedding latency, cold-start users, and scalable data pipelines (ONNX, PySpark/Delta Lake, caching)
- Strong MLOps/orchestration experience (Airflow DAGs, AWS Step Functions, SageMaker deployments; Prefect and Kubernetes CronJobs)
- Rigorous evaluation and reliability approach for AI workflows (offline + adversarial evals, shadow deploys, A/B tests, monitoring/guardrails/fallbacks)
- Pragmatic model/retrieval/prompt selection based on constraints (latency, accuracy, context length, data availability)
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