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.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
