Hard Skills

- Blockchain Data Modeling & Analytics

- Machine Learning Algorithms (Supervised & Unsupervised)

- Deep Learning (CNNs, RNNs, Transformers)

- Smart Contract Security & Auditing (Solidity, Vyper)

- Distributed Systems & Consensus Protocols

- Data Engineering (ETL, Data Lakes)

- Python, TensorFlow, PyTorch, scikit-learn

- Crypto and Hashing Algorithms (SHA-256, ECDSA)

- Cloud Platforms (AWS, GCP)

- Containerization & CI/CD (Docker, Jenkins)

Soft Skills

- Analytical & Critical Thinking

- Cross-Functional Collaboration

- Agile Methodologies

- Innovative Problem Solving

- Effective Communication

- Ethical AI Implementation

*Senior Machine Learning Engineer | BlockSecure Technologies — San Francisco, CA*

Jan 2023 – Present

- Led development of ML models for anomaly detection in blockchain transaction networks, reducing fraud incidents by 35%.

- Designed NLP-based tools for the automated auditing of smart contracts, increasing audit speed by 50%.

- Implemented federated learning techniques to enable privacy-preserving data analysis across multiple blockchain nodes.

- Collaborated with security teams to develop ML-driven threat detection systems for blockchain infrastructure.

*Machine Learning Engineer | CryptoInsight Labs — New York, NY*

Jun 2020 – Dec 2022

- Built predictive models to assess DeFi protocol risks using on-chain activity and user behavioral data, improving risk mitigation strategies.

- Developed clustering algorithms to identify emerging DeFi projects and related token movements, aiding investment decisions.

- Integrated machine learning pipelines with blockchain data streams for real-time analytics.

- Worked closely with smart contract developers to embed AI insights into transaction validation processes.

*Data Scientist / Machine Learning Specialist | DecentralAI Inc. — Boston, MA*

Aug 2017 – May 2020

- Designed deep learning models to analyze blockchain transaction graphs, uncovering fraudulent activity with 92% accuracy.

- Developed a recommendation engine for decentralized applications, enhancing user engagement metrics.

- Led blockchain data engineering initiatives, creating scalable ETL pipelines for large-scale on-chain data ingestion.

- Participated in research for zero-knowledge proof optimization using ML techniques.

**Master of Science in Computer Science** — MIT

Specialization in AI & Distributed Systems | 2015 – 2017

**Bachelor of Science in Software Engineering** — University of California, Berkeley

2011 – 2015

- Certified Blockchain Professional (CBP) — Blockchain Council, 2021

- TensorFlow Developer Certificate — Google, 2022

- Certified Data Scientist — Data Science Council of America (DASCA), 2020

- **AI-Powered Smart Contract Vulnerability Scanner:** Developed a machine learning system that analyzes smart contract bytecode for potential vulnerabilities, reducing manual review time by 60%.

- **Decentralized Fraud Detection Framework:** Created an ML-powered platform integrating graph neural networks to identify suspicious activities across multiple DeFi platforms with real-time alerts.

- **Predictive DeFi Market Analytics:** Built models forecasting token price movements based on on-chain metrics, improving portfolio risk assessment tools used by institutional investors.

Python, TensorFlow, PyTorch, scikit-learn, Solidity, Vyper, Kubernetes, Docker, Apache Spark, Kafka, AWS (S3, EC2, Lambda), GCP (BigQuery, Cloud ML), Graph Neural Networks, ZKP Frameworks (ZoKrates, Bulletproofs)

English (Native) | Spanish (Professional Working Proficiency)