Machine Learning Engineer

Hard Skills

- Deep Learning Frameworks: TensorFlow, PyTorch, Keras

- Data Science & Analytics: Pandas, NumPy, scikit-learn

- Model Deployment: Docker, Kubernetes, AWS SageMaker, MLflow

- NLP Techniques: BERT, GPT, Transformer architectures

- Computer Vision: CNNs, Object Detection, OpenCV

- Data Engineering: Spark, Kafka, SQL, NoSQL

- Version Control & CI/CD: Git, Jenkins, GitLab CI

Soft Skills

- Problem-solving & Critical Thinking

- Cross-team Collaboration

- Agile Methodologies

- Effective Communication of Complex Concepts

- Ethical AI and Bias Mitigation

**Master of Science in Computer Science**

*Massachusetts Institute of Technology (MIT)*, Cambridge, MA | 2014 – 2016

**Bachelor of Science in Mathematics**

*University of California, Berkeley*, Berkeley, CA | 2010 – 2014

- AWS Certified Machine Learning – Specialty (2023)

- Certified TensorFlow Developer (2021)

- AI Ethics and Fairness Certification – Coursera (2022)

Real-Time Anomaly Detection System

Developed a scalable streaming system using Kafka and Spark to detect fraudulent transactions at scale, leading to a 40% decrease in financial losses. Integrated deep learning-based anomaly detection models with a user-friendly dashboard.

Natural Language Understanding for Customer Feedback

Engineered an NLP pipeline leveraging BERT embeddings to classify and analyze sentiment from millions of customer reviews, enabling targeted marketing strategies that increased customer satisfaction scores by 15%.

Automated Image Labeling for Medical Imaging

Led the creation of a computer vision pipeline using CNNs for labeling X-ray images, accelerating diagnostic workflows and supporting radiologist decision-making in a hospital network.

- Python, R, Bash

- TensorFlow, PyTorch, Keras

- Docker, Kubernetes, AWS (SageMaker, Lambda)

- Spark, Kafka, Hadoop

- SQL, MongoDB, DynamoDB

- Jupyter, Zeppelin

- English (Fluent)

- Spanish (Professional Proficiency)