Available to hire
I am an AI/ML Engineer with 5 years of experience building multi-agent systems, large-scale LLM inference platforms, and real-time financial ML solutions.
I thrive on designing scalable, data-driven architectures and collaborating with cross-functional teams to optimize throughput and latency, deploying models on AWS and ensuring robust MLOps practices.
Skills
Language
English
Fluent
Work Experience
ML/Data Engineer at McKinsey & Co.
June 1, 2023 - PresentDesigned and developed multi-tiered enterprise data applications, built scalable ETL pipelines using Python, SQL, and Spark to process large volumes of transactional, behavioral, and risk-related data across financial systems; developed ingestion pipelines for unstructured data; built real-time Kafka pipelines; designed data models and analytical marts enabling fraud analytics, identity scoring, and risk monitoring; integrated vector databases into enterprise search and RAG systems for internal knowledge retrieval; automated validation, schema enforcement, and data quality checks; containerized and deployed data services with Kubernetes and Azure Functions; partnered with data scientists to build feature pipelines and ML workflows; contributed to benchmarking frameworks and model performance analysis.
ML/Data Engineer at PNC Bank
May 1, 2019 - August 1, 2021Developed backend services for financial transaction processing; built LLM inference and embedding pipelines on Azure; developed ETL and text-processing workflows for PDFs, logs, and telemetry; designed RAG pipelines with chunking strategies and embedding storage; built high-throughput data ingestion for fraud analytics and risk scoring (30M+ daily transactions); implemented ETL pipelines for aggregating customer, device, merchant, and geo-behavioral data; created logging and telemetry for anomaly detection; designed data models for fraud investigations and risk classification; built real-time ML scoring pipelines; created PDF/document extraction pipelines; integrated vector databases for semantic search; implemented Airflow orchestration, data quality monitoring, and dashboards; delivered FastAPI gateways and TensorFlow Serving endpoints to support real-time case management.
AI/ML Engineer at McKinsey & Co.
June 1, 2023 - PresentDesigned and developed multi-tiered data and analytics applications that improved processing efficiency by 35% for consulting workflows. Built a data-centric multi-agent AI platform supporting trading, risk, compliance, and customer analytics on structured financial datasets. Deployed scalable agent and model-serving infrastructure on AWS (Lambda and containerized services) with sub-2-second latency for 50,000+ daily queries. Partnered with infra teams to deploy LLMs on AWS Trainium/Inferentia and tune batching/parallelism, improving throughput by 35% and optimizing resource utilization. Developed internal ML tooling to profile and debug accuracy-performance tradeoffs across hardware accelerators. Collaborated with data science teams to tune model parallelism and batching techniques for enterprise-scale inference workloads. Contributed to benchmarking frameworks measuring end-to-end model performance and resource utilization across multi-node deployments.
ML Engineer at PNC Bank
May 1, 2019 - August 1, 2021Developed core backend services for financial transaction processing, improving system throughput by 45% and reducing latency by 30%. Built real-time fraud detection models for 30M+ daily transactions, improving precision to 98% and reducing false positives by 35%. Built ensemble fraud models combining gradient boosting (XGBoost) and deep neural networks, achieving 94% precision and 87% recall while reducing false positives by 52%. Deployed PyTorch and TensorFlow models on AWS SageMaker with autoscaling, maintaining 99.9% uptime across peak loads. Accelerated inference using quantization and pruning, cutting latency by 30–45% and lowering compute cost by 40%. Implemented end-to-end ML CI/CD with GitHub Actions, Docker, Terraform, and Helm, reducing deployment cycles from days to under three hours. Set up MLflow experiment tracking and a model registry with approval gates for risk and compliance, ensuring full lineage and auditability. Delivered production monitoring for data quality,
Education
M.S. Applied Statistics & Decision Analytics at Wester Illinois University
January 11, 2030 - February 9, 2026M.S. Applied Statistics & Decision Analytics at Western Illinois University
January 11, 2030 - February 9, 2026Qualifications
Industry Experience
Financial Services, Professional Services, Software & Internet
Skills
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