Open to opportunities

Taylor Smith

@taylorsmith

Message

Senior AI/ML engineer specializing in production ML, fraud detection, and LLMs.

United States

Message

What I'm looking for

I seek senior AI/ML roles building scalable, compliant ML systems—focus on production impact, cross-functional leadership, MLOps best practices, and opportunities to mentor and drive responsible AI adoption.

I am a Senior AI/ML engineer with over a decade of experience designing and deploying machine learning systems in production across fintech and enterprise environments. I have led initiatives in fraud detection, risk modeling, and NLP-driven automation using Python, PyTorch, XGBoost, and modern LLMs.

I architect scalable, real-time ML pipelines and cloud infrastructure on AWS, GCP, and Kubernetes, and I build compliant, explainable AI solutions—applying SHAP, federated learning, and regulatory audits to meet FCRA and GDPR requirements. I have improved latency and model performance through feature-store optimization, caching, and hyperparameter tuning.

I mentor engineers, run workshops on MLOps and responsible AI, and drive cross-functional adoption of generative AI and governance frameworks to align technical delivery with business goals.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Dentsu

Jan 2023 - Present (2 years 8 months)

Lead end-to-end AI/ML initiatives for clients focused on fraud detection, risk modeling, and personalization, architecting real-time pipelines on AWS and integrating LLMs while ensuring regulatory compliance (FCRA, GDPR).

Python PyTorch AWS Sagemaker SHAP Federated Learning MLOps

AI/ML Engineer

Accenture

Dec 2021 - Jan 2023 (1 year 1 month)

Developed NLP and deep learning models to automate dispute classification and improve fraud prediction, implemented Optuna hyperparameter tuning and ANN/vector DBs to accelerate similarity search and reduce scoring latency by 23%.

NLP PyTorch Optuna ANN Docker Model Monitoring Vector Databases CI CD

Machine Learning Engineer

Accenture

Jun 2017 - Dec 2021 (4 years 6 months)

Led architecture and deployment of real-time fraud detection systems using XGBoost/LightGBM and scalable ML infrastructure on GCP/Kubernetes, reducing inference time up to 30% and improving model governance with MLFlow.

XGBoost LightGBM GCP)Kubernetes Apache Airflow MLFlow Model Explainability (SHAP LIME)

Data Scientist

Stripe

Oct 2015 - Jun 2017 (1 year 8 months)

Built and validated fraud detection and risk assessment models, reduced false positives by 12% through feature engineering, and created automated data pipelines and dashboards to inform product and risk decisions.