Hard Skills

- Machine Learning & Deep Learning (TensorFlow, PyTorch, scikit-learn)

- Cloud Platforms (AWS, Azure, GCP)

- Containerization & Orchestration (Docker, Kubernetes)

- CI/CD Tools (Jenkins, GitOps, ArgoCD)

- Infrastructure as Code (Terraform, CloudFormation)

- Data Pipelines & ETL Processes

- Model Monitoring & A/B Testing

- Python, Bash, SQL

- Version Control (Git)

Soft Skills

- Problem-Solving & Analytical Thinking

- Cross-Functional Collaboration

- Agile & DevOps Methodologies

- Documentation & Communication

- Continuous Learning & Adaptability

*Senior Machine Learning Engineer | InnovateAI Solutions, New York, NY*

June 2022 – Present

- Lead the deployment of real-time ML inference pipelines on Kubernetes clusters, reducing latency by 30% for high-priority financial fraud detection models.

- Built end-to-end CI/CD workflows integrating Jenkins, Docker, and ArgoCD, enabling MLOps automation and rapid model updates—cut deployment time from days to hours.

- Spearheaded cloud migration strategies from on-prem to AWS SageMaker, improving scalability and resource utilization by 25%.

- Implemented model versioning and monitoring frameworks, resulting in early detection of model drift and maintaining model accuracy above 95%.

*Machine Learning Engineer | TechBay Analytics, San Francisco, CA*

August 2018 – May 2022

- Developed machine learning models for predictive analytics in customer engagement, increasing prediction accuracy by 15% over previous benchmarks.

- Automated ML workflows using Python scripts integrated into Jenkins pipelines, which accelerated model training cycles by 40%.

- Managed cloud infrastructure for data storage and processing on GCP, optimizing costs by restructuring data pipelines and storage classes.

- Collaborated with DevOps teams to containerize ML applications and ensure seamless deployment on Kubernetes clusters.

*Data Scientist & ML Developer | DataCore Inc., Austin, TX*

July 2016 – July 2018

- Designed and deployed anomaly detection models for IoT sensor data, reducing false positives by 20%.

- Participated in cross-team development of a scalable ETL pipeline using Apache Beam and Dataflow, handling over 10TB of data daily.

- Conducted A/B testing for marketing models, providing insights that supported targeted campaign strategies.

**Master of Science in Computer Science**

University of Texas at Austin, TX | 2014 – 2016

**Bachelor of Science in Electrical Engineering**

University of California, Berkeley, CA | 2010 – 2014

- AWS Certified Machine Learning – Specialty (2023)

- Kubernetes Administrator Certification (2022)

- Certified TensorFlow Developer (2021)

- **AI-Driven Fraud Detection System:** Designed a scalable ML-centric fraud detector leveraging unsupervised learning techniques, deployed on Kubernetes and integrated with real-time transaction streams.

- **Model Monitoring Platform:** Developed an open-source tool for tracking model drift and performance degradation, reducing troubleshooting time by 50%.

- **Automated Data Labeling Pipeline:** Created a semi-supervised labeling framework that automated data annotation for large datasets, improving labeling throughput and accuracy.

- **ML Frameworks:** TensorFlow, PyTorch, scikit-learn, XGBoost

- **Cloud & DevOps:** AWS, GCP, Azure, Docker, Kubernetes, Terraform, Jenkins, GitOps

- **Data & Orchestration:** Apache Beam, Airflow, Spark, Hadoop

- **Languages:** Python, Bash, SQL, YAML

- English (Native)

- Spanish (Fluent)