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Monisha Nettem
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
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About
AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.
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Key Strengths
- Built AI-driven credit-risk prediction and reporting system linking structured transactions with unstructured financial/compliance documents
- Deployed RAG pipeline (LangChain + GPT-4) to reduce hallucinations and improve explainability
- Improved risk detection/model precision by ~20% and reduced manual reporting/analysis time by ~50%
- Designed entity resolution and data cleaning pipeline using fuzzy matching (TF-IDF/cosine) and outlier detection (Z-score/IQR), improving data consistency ~35%
- Productionized ML workflows with Airflow/Kubeflow, CI/CD (GitHub Actions), Docker, and cloud orchestration on GCP/AWS
- Experimentation-to-production decision-making using MLflow tracking and business metrics; reduced model turnaround time by ~40%
- Implemented embeddings + vector DB retrieval (FAISS/Pinecone; OpenAI/Sentence-BERT) improving semantic search relevance by 30%+
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