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Sana Khan
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time inference in FinTech
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About
ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.
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Key Strengths
- Built and deployed an LLM-based query understanding + routing assistant for order recommendations
- Handled noisy/variable user input via transformer fine-tuning plus embedding-based retrieval
- Production-focused ML evaluation: precision/recall/F1 plus latency and error-rate monitoring
- Iterative improvement using user feedback, low-confidence cases, and refined labeling/training data
- Designed and maintained Airflow ETL pipelines with retries, alerting, and data-quality validation
- Optimized data processing performance (joins, partitioning, column reduction) to reduce runtime
- Made model trade-offs for real-time latency by switching from larger models to distilled models
- Used MLflow experiment tracking to compare versions and gate deployments
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