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Aravind Gudipudi
Mid-level AI/ML Engineer specializing in MLOps and cloud-deployed ML systems
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About
ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.
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Key Strengths
- Built and deployed production NLP system to reduce manual, error-prone data handling
- Designed end-to-end ML pipelines (ingestion, preprocessing, training, evaluation, auto-retraining)
- Maintained model performance over time via monitoring and retraining to handle data drift
- Optimized production inference for low latency
- Strong workflow reliability practices: staged pipelines with defined I/O and validation checks; reproducibility focus
- Pragmatic model selection approach (baseline traditional ML before deep learning)
- Effective collaboration with non-technical stakeholders using dashboards/visualizations; incorporated feedback to tune thresholds
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