Matthew Devoto

Skills

Languages: Python (5+ years), SQL (PostgreSQL, BigQuery), Bash, C++
Frameworks/Libraries:
● ML: scikit-learn, OpenCV, NLTK

● Deep Learning: PyTorch, TensorFlow, Keras, spaCy

● Transformers & Modern AI: Hugging Face Transformers, FastAI, Detectron2, timm

● API Development: FastAPI, Pydantic, Flask
Cloud & Infrastructure: AWS (SageMaker, Lambda, S3, EC2), GCP (Vertex AI, Storage, Compute Engine), Azure ML, Docker, Kubernetes, Terraform
Databases: PostgreSQL, MySQL, MongoDB, Elasticsearch, Redis, Neo4j, BigQuery
DevOps & Orchestration: MLflow, DVC, Kubeflow, Airflow, Git, GitHub Actions, GitLab CI/CD, Prometheus, ELK Stack, Weights & Biases
Practices & Tools: Agile, ML lifecycle management, Model Deployment, RESTful APIs, Microservices, Data Pipeline Development, Data Ingestion & Transformation, Model Monitoring, Automation, Agentic AI Systems, LLM Fine-tuning, Prompt Engineering, Evaluation Metrics (BLEU, ROUGE, F1-score)

About

Machine Learning Engineer with deep technical expertise in building scalable AI systems and deploying robust machine learning pipelines. Over a decade of experience spanning traditional ML, deep learning, and modern MLOps, with strong proficiency in Python, cloud infrastructure (AWS, GCP), and ML lifecycle management. Expert in developing RESTful APIs (FastAPI, Pydantic), orchestrating containerized workflows with Docker and Kubernetes, and managing complex data pipelines using Airflow. Passionate about designing agentic AI systems and deploying large language models (LLMs) for practical business impact.
Known for transforming cutting-edge research into production-grade solutions, optimizing models for performance, reliability, and cost-efficiency. Skilled at collaborating across engineering, product, and data science teams to deliver end-to-end machine learning solutions that drive measurable results. Thrive in agile, fast-paced environments focused on innovation, automation, and scalability.