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Lakshmi Meghana
Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP
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About
Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.
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Key Strengths
- Built and deployed production NLP system for clinical note classification in a HIPAA-regulated healthcare environment
- Strong data quality and preprocessing approach (PHI masking, boilerplate removal, deduplication, abbreviation standardization, label cleanup)
- Production MLOps and orchestration experience with Airflow on AWS (reliable DAGs, dependencies, retries, alerting)
- Improved pipeline robustness by adding data readiness checks and making tasks idempotent to prevent failures from late/incomplete upstream data
- Performance-focused production deployment (low-latency inference via batching/model optimization; monitoring with MLflow and CloudWatch)
- Structured evaluation mindset for agents/workflows (unit + E2E + regression tests; offline metrics; reliability/latency/cost monitoring; error analysis; A/B tests and rollback)
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