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Tejaswini P
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and MLOps
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Built and deployed an LLM-powered financial/regulatory document analysis platform at State Street, combining fine-tuned transformer models with a RAG pipeline over internal knowledge bases. Owned the productionization stack (FastAPI, Docker, SageMaker, Terraform, CI/CD) plus monitoring for drift/latency/hallucinations, delivering ~40% faster analyst review and improved reliability through chunking/embeddings and grounding.
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Key Strengths
- Deployed LLM/RAG system to production on AWS SageMaker with CI/CD and Terraform
- Improved analyst review time by ~40% via automated extraction/summarization/anomaly detection
- Solved long-document token limit issues using chunking + embeddings-based retrieval
- Reduced hallucinations by grounding outputs with RAG against internal knowledge bases
- Balanced latency and cost by fine-tuning smaller models and adding caching
- Built robust monitoring for latency, drift, and hallucination rates (CloudWatch, MLflow, Prometheus)
- Orchestrated end-to-end ML workflows with Airflow and event-driven AWS Step Functions/Lambda
- Effective collaboration with product and compliance stakeholders using plain-language alignment and iterative feedback
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