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Asrith Velireddy
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems
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ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.
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Key Strengths
- Built and deployed transformer-based recommendation system in production at Adobe
- Scaled data pipelines to process 12M+ behavioral events/day
- Production-grade MLOps: containerized inference on AWS SageMaker with Docker/Kubernetes
- Model drift strategy: monitoring dashboards, baseline comparisons, data validation, controlled retraining
- Experiment-driven iteration using MLflow for reproducible model versioning and deployment
- Designed measurable evaluation frameworks for LLM/RAG workflows (success rate, relevance, latency, hallucination rate)
- Cross-functional communication: translated marketing/product goals into ML metrics and dashboards
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