Machine Learning Engineer with 5+ years of experience architecting high-scale Generative AI (RAG) and distributed ML pipelines across financial and tech ecosystems. Expert in plumbing real-time data streams via Kafka and…
Machine Learning Engineer with 5+ years of experience architecting high-scale Generative AI (RAG) and distributed ML pipelines across financial and tech ecosystems. Expert in plumbing real-time data streams via Kafka and Spark into Transformers and GNN models, achieving sub-50ms inference latencies and $2M+ in measurable cost avoidance. Proven track record in operationalizing MLOps—utilizing Kubernetes, Terraform, and MLflow—to scale enterprise-grade predictive engines from 10k to 1M+ daily active requests.
Architected an enterprise-grade RAG pipeline utilizing LangChain and Llama-3 to semantically index 2.5TB of unstructured financial regulations.
Deployed the pipeline on AWS SageMaker.
Reduced manual query response times by 40%.
Maintained 98% factual accuracy.
Developed a high-performance GNN using PyTorch Geometric to analyze 10M+ transactional nodes for real-time fraud detection.
Integrated Apache Kafka for streaming.
Prevented $2.5M in annual losses.
Achieved sub-200ms inference latency across clusters.
Optimized multi-variate Time Series Forecasting using Prophet and PySpark on Databricks to predict quarterly liquidity requirements across 500+ branches.
Achieved a 95% confidence interval in cash flow projections.
Improved strategic capital allocation by 15%.
Engineered an automated customer segmentation engine utilizing K-Means clustering and XGBoost on 12TB Amazon Redshift.
Identified high-propensity cohorts for personalized loans.
Resulted in a 22% increase in cross-sell conversions and $1.2M revenue.
Orchestrated a scalable MLOps using Kubernetes, Docker, and Terraform to automate the deployment lifecycle of 50+ production models.
Integrated MLflow for tracking.
Accelerated model time-to-market by 70%.
Reduced infrastructure overhead by 25%.
Implemented a BERT-based NLP engine via FastAPI to perform real-time sentiment analysis on 1M+ monthly customer feedback entries.
Plumbed data using AWS Glue.
Provided actionable CX insights.
Led to a 30% reduction in support.
Built distributed ETL pipelines utilizing Apache Spark and Delta Lake to process 15TB of raw transactional telemetry.
Automated orchestration with Airflow.
Ensured 99.9% data reliability.
Accelerated downstream feature engineering availability by 50%.
Architected an enterprise-grade RAG pipeline utilizing LangChain and Llama-3 to semantically index 2.5TB of unstructured financial regulations; deployed on AWS SageMaker, reducing manual query response times by 40% while maintaining 98% factual accuracy., Developed a high-performance GNN using PyTorch Geometric to analyze 10M+ transactional nodes for real-time fraud detection; integrated Apache Kafka for streaming, preventing $2.5M in annual losses with sub-200ms inference latency across clusters., Optimized multi-variate Time Series Forecasting using Prophet and PySpark on Databricks to predict quarterly liquidity requirements across 500+ branches; achieved a 95% confidence interval in cash flow projections, improving strategic capital allocation by 15%., Engineered an automated customer segmentation engine utilizing K-Means clustering and XGBoost on 12TB Amazon Redshift; identified high-propensity cohorts for personalized loans, resulting in a 22% increase in cross-sell conversions and $1.2M revenue., Orchestrated a scalable MLOps using Kubernetes, Docker, and Terraform to automate the deployment lifecycle of 50+ production models; integrated MLflow for tracking, accelerating model time-to-market by 70% and reducing infrastructure overhead by 25%., Implemented a BERT-based NLP engine via FastAPI to perform real-time sentiment analysis on 1M+ monthly customer feedback entries; plumbed data using AWS Glue, providing actionable CX insights that led to a 30% reduction in support., Built distributed ETL pipelines utilizing Apache Spark and Delta Lake to process 15TB of raw transactional telemetry; automated orchestration with Airflow, ensuring 99.9% data reliability and accelerating downstream feature engineering availability by 50%.
Designed a recommendation system using LightGBM and Scikit-Learn for a high-traffic e-commerce platform.
Processed 5M+ user profiles to deliver real-time product suggestions.
Achieved an 18% uplift in Average Order Value and $800K incremental growth.
Developed a Convolutional Neural Network (CNN) using TensorFlow and OpenCV to automate visual product search and image tagging for 2M+ digital assets.
Achieved 94% precision in classification.
Drove a 20% increase in user platform engagement.
Executed Bayesian Inference and A/B/n testing frameworks using PyMC3 to evaluate dynamic pricing strategies across diverse market segments.
Identified statistically significant revenue drivers.
Led to a 12% boost in quarterly customer retention.
Constructed a predictive churn model utilizing LSTM and RNN architectures on SQL Server datasets to analyze sequential user behavioral patterns.
Accurately identified at-risk customers with 89% recall.
Enabled intervention strategies that reduced churn.
Applied unsupervised DBSCAN clustering in Python to detect anomalies within logistics data for a major client.
Identified 5% systemic inefficiencies and fraudulent shipping patterns.
Resulted in $500K annual cost avoidance and optimized routing strategies.
Architected a BI reporting suite utilizing Power BI, DAX, and PostgreSQL to visualize multi-dimensional sales performance metrics for C-suite stakeholders.
Automated data refresh cycles.
Reduced manual reporting overhead by 40%.
Streamlined automated data cleansing and transformation pipelines using Python and SciPy for 5TB raw datasets stored in AWS S3.
Improved data signal-to-noise ratios.
Led to a 60% acceleration in model training cycles and higher precision.
Designed a recommendation system using LightGBM and Scikit-Learn for a high-traffic e-commerce platform; processed 5M+ user profiles to deliver real-time product suggestions, achieving an 18% uplift in Average Order Value and $800K incremental growth., Developed a Convolutional Neural Network (CNN) using TensorFlow and OpenCV to automate visual product search and image tagging for 2M+ digital assets; achieved 94% precision in classification, driving a 20% increase in user platform engagement., Executed Bayesian Inference and A/B/n testing frameworks using PyMC3 to evaluate dynamic pricing strategies across diverse market segments; identified statistically significant revenue drivers, leading to a 12% boost in quarterly customer retention., Constructed a predictive churn model utilizing LSTM and RNN architectures on SQL Server datasets to analyze sequential user behavioral patterns; accurately identified at-risk customers with 89% recall, enabling intervention strategies that reduced churn., Applied unsupervised DBSCAN clustering in Python to detect anomalies within logistics data for a major client; identified 5% systemic inefficiencies and fraudulent shipping patterns, resulting in $500K annual cost avoidance and optimized routing strategies., Architected a BI reporting suite utilizing Power BI, DAX, and PostgreSQL to visualize multi-dimensional sales performance metrics for C-suite stakeholders; automated data refresh cycles, reducing manual reporting overhead by 40%., Streamlined automated data cleansing and transformation pipelines using Python and SciPy for 5TB raw datasets stored in AWS S3; improved data signal-to-noise ratios, leading to a 60% acceleration in model training cycles and higher precision.
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