• Engineered a Retrieval-Augmented Generation (RAG) system using GPT-4 and LangChain to analyze complex financial regulations and advisory documents, cutting research time for financial advisors by 35%.

• Implemented an enterprise-grade MLOps platform utilizing Kubeflow and AWS SageMaker, standardizing CI/CD and feature versioning with MLflow, which accelerated the model iteration and deployment cycle by 45%.

• Developed Time Series Forecasting models (Prophet, ARIMA) in Python (statsmodels) to predict short-term currency volatility for the FX desk, yielding a 12% improvement in hedging strategy performance.

• Applied NLP and Deep Learning (BERT) models using PyTorch to automatically classify and tag unstructured compliance documentation, reducing manual review effort by 60% and ensuring 99.9% audit trail accuracy.

• Integrated SHAP and LIME frameworks into high-stakes Classification models (credit/risk) to provide transparent, explainable decisions, boosting stakeholder trust and model adoption rates by 20%.

• Designed a real-time feature delivery pipeline using Apache Kafka and PySpark on AWS EC2 to ingest and process market event data, achieving sub-50ms latency for consumption by production models.

• Optimized data models and performed Feature Engineering on Snowflake data warehouse, implementing efficient PL/SQL procedures that improved the feature extraction speed for model training by 30%.

• Designed a Deep Learning model (CNN using Keras and TensorFlow) integrated with OpenCV for automated defect detection in manufacturing images, achieving 95% accuracy and decreasing product inspection time by 50%.

• Fine-tuned a smaller LLM (BERT or a custom model using LoRA and Hugging Face) for domain-specific text generation and summarization, improving internal knowledge search efficiency by 40%.

• Developed production-ready REST APIs (FastAPI) for inference, containerized services using Docker, and orchestrated deployment on GCP (Vertex AI, Kubernetes), handling 10,000+ daily requests.

• Built a Regression/Classification ensemble model using scikit-learn and Python to predict customer churn, achieving an AUC-ROC of 0.88 and guiding retention efforts that saved the client an estimated $22K annually.

• Implemented MLflow for tracking all model experiments, parameters, and metrics, creating a centralized Model Registry that reduced experimental setup time for new engineers by 25%.

• Led A/B Testing and Statistical Analysis for model performance and business impact validation in a live production environment, ensuring only models with a statistically significant performance lift of 5% or more were deployed.

• Designed and automated ETL/ELT workflows using Airflow to manage data transfer and Data Wrangling of over 2TB of data from PostgreSQL & MySQL to a central analytics platform.