Machine Learning Engineer for Financial AI

MAYOWA OYEWOLE

Machine Learning Engineer AI/ML Data Science MLOps

309-***-**** **************@*****.***

www.linkedin.com/in/mayowa-oyewole-02b6b2224

PROFESSIONAL SUMMARY

Results-driven Machine Learning Engineer and AI practitioner with strong experience designing, deploying, and scaling AI/ML solutions in financial services and regulated environments. Proven ability to translate complex business problems into production-grade AI systems, including predictive modeling, NLP, fraud detection, and LLM-powered applications.

Hands-on expertise in MLOps, cloud platforms (AWS), big data processing, and model lifecycle management, with a strong focus on model performance, explainability, and governance. Experienced in building RAG pipelines, agentic AI systems, and real-time inference APIs that deliver measurable business impact.

CORE SKILLS & TECHNOLOGIES

Machine Learning & AI: Machine Learning, Deep Learning, NLP, Generative AI, LLMs, Transformer Models, Neural Networks RAG (Retrieval-Augmented Generation), Agentic AI, Supervised & Unsupervised Learning, Model Evaluation, Feature Engineering, Model Optimization, Explainable AI (XAI)

Programming & Frameworks: Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, FastAPI

MLOps & Deployment: MLflow, Docker, Kubernetes, CI/CD Pipelines, Model Monitoring, Data Drift Detection, Model Versioning, DVC, REST APIs, Microservices Architecture

Cloud & Big Data: AWS (SageMaker, Lambda, S3, EC2, EKS, API Gateway, CloudWatch), Databricks, Snowflake, Spark, Hadoop

Data Engineering & Analytics: Data Modeling, ETL Pipelines, Data Warehousing, Feature Stores, A/B Testing, Time Series Analysis, Statistical Modeling, Data Visualization

Tools & Platforms: Power BI, Tableau, Git, GitHub, Jupyter Notebook, Streamlit, Airflow, FAISS, Vector Databases

Domain Expertise: Financial Services, Risk Modeling, Fraud Detection, Loan Analytics, Regulatory Compliance, Credit Risk, Transaction Monitoring

PROFESSIONAL EXPERIENCE

JP MORGAN CHASE - New York, NY

Senior Machine Learning Engineer Dec 2024 - Present

Architected an AI-powered regulatory intelligence platform to process Federal Register data for automated policy analysis.

Built NLP classification models (TF-IDF, Logistic Regression) to analyze sentiment and regulatory impact.

Generated semantic embeddings (Sentence Transformers (MiniLM)) and implemented FAISS-based vector search, improving retrieval speed and accuracy.

Developed RAG pipelines with LLM integration, improving contextual insights and explainability.

Integrated LLMs (Claude API) to generate human-readable regulatory insights.

Designed and orchestrated agentic AI workflows using LangGraph for dynamic decision-making.

Built fraud detection and anomaly detection models on transaction datasets using supervised and unsupervised learning.

Applied deep learning techniques to uncover hidden transaction patterns.

Processed large-scale data using Spark/PySpark, improving performance and scalability.

Implemented MLflow and DVC for experiment tracking and reproducibility.

Containerized ML solutions using Docker for consistent deployment.

Developed Power BI dashboards to visualize KPIs, fraud trends, and compliance metrics.

MORGAN STANLEY – Alpharetta, GA

Machine Learning Engineer / Quantitative Analyst Sept 2022 - Nov 2024

Designed and deployed an end-to-end ML pipeline for loan approval prediction using structured financial data, improving decision consistency and automation.

Performed advanced feature engineering (FICO, DTI, LTV) and data preprocessing, improving model readiness and data quality.

Built and evaluated models (Logistic Regression, Random Forest, Gradient Boosting, SVM), improving classification performance and model robustness.

Applied class imbalance techniques (SMOTE, class weighting) to enhance minority class prediction accuracy.

Implemented model evaluation framework using Accuracy, Precision, Recall, F1-score, and ROC-AUC.

Established champion/challenger model strategy using MLflow for continuous performance benchmarking.

Developed scalable pipelines using Spark/PySpark, reducing processing time for large datasets.

Built real-time inference APIs using AWS Lambda and API Gateway for production model serving.

Deployed models via AWS SageMaker endpoints, enabling low-latency predictions.

Implemented MLOps best practices including CI/CD pipelines, automated retraining, and monitoring.

Built RAG-based AI system combining predictive models with contextual data retrieval for decision support.

Designed agentic workflows using LangGraph, enabling multi-step reasoning and automation.

TOYOTA FINANCIAL SERVICES - Plano, TX

Quantitative Analyst Jun 2021 - Aug 2022

Analyzed loan servicing data to identify prepayment risk, delinquency trends, and portfolio performance.

Built analytical datasets using SQL and Snowflake to support predictive modeling.

Designed data models (star & snowflake schemas) for reporting and ML pipelines.

Conducted statistical analysis and data validation to improve data quality and reliability.

Developed Power BI dashboards to track KPIs and operational performance.

Automated ETL pipelines and data workflows, improving efficiency and reducing manual effort.

Engineered text features using TF-IDF and transformer embeddings, enhancing model performance.

Built hybrid ML models combining structured and unstructured data.

NIGERIAN NATIONAL PETROLEUM CORPORATION (NNPC)

Business Data Analyst Apr 2016 - Jul 2019

Delivered enterprise analytics and dashboards supporting CRM and cloud migration initiatives.

Implemented data governance and quality control processes to ensure data consistency.

Supported AWS migration projects through data validation and reconciliation.

Automated reporting using SQL, Excel, and Power BI, improving reporting efficiency.

Documented ETL processes and data transformation rules for reproducibility.

EDUCATION

Master of Science, Quantitative Economics - Western Illinois University, Macomb, IL - 2021

Bachelor of Science, Economics - Obafemi Awolowo University - 2014

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