Maverick Mason

AI/ML Engineer with 8+ years of experience designing, training, and deploying machine learning, deep learning, and generative AI systems, including LLM (GPT) applications, RAG pipelines, agentic workflows, computer vision, and NLP solutions. Skilled in Python, prompt engineering, fine-tuning (LoRA/PEFT), and vector databases, with hands-on experience across the full ML lifecycle from data wrangling and model evaluation to MLOps, CI/CD, and cloud-based production deployment on AWS, Azure, and GCP using Docker and Kubernetes. Proven track record of technical leadership, cross-functional collaboration, and mentoring engineering teams to deliver scalable, well-evaluated AI solutions.

Technical Lead – Lead AI Engineer - Painter Marketing Pros

(2024-06)

• Lead design and delivery of production AI/ML systems, LLM-powered/agentic applications, RAG pipelines, and generative AI features, driving a 30% increase in feature delivery velocity across cross-functional teams.
• Architect MLOps infrastructure using Docker, Kubernetes, and Terraform (IaC), and design CI/CD pipelines with GitHub Actions for automated testing and rollback, cutting deployment time 40% and lifting uptime to 99.9%.
• Provide technical leadership and mentorship to 6 engineers on ML best practices, Python code quality, and system design, reducing code review turnaround time by 25%.
• Evaluate and integrate emerging LLM/generative AI tooling, including prompt engineering frameworks, vector databases, LoRA/PEFT fine-tuning, and agent orchestration, into the roadmap, cutting model iteration time by 35%.
• Establish evaluation and monitoring frameworks for LLM applications, adding observability instrumentation (logging, metrics, tracing) that cut incident detection time 50% and lifted output quality scores 20%.
• Design modular microservices-based APIs (REST, GraphQL) serving ML models to 2M+ daily inference requests, securing endpoints with OAuth2/JWT and integrating PostgreSQL and Kafka event pipelines.
• Architect cloud infrastructure across AWS, Azure, and GCP, applying IAM and cybersecurity best practices for scalable ML workloads, reducing infrastructure costs by 22%.
• Build internal ML monitoring dashboards and developer tooling using React, TypeScript, and Node.js, cutting mean-time-to-resolution 30% and translating stakeholder requirements into scalable ML/AI solutions.
• Represent the AI/ML function in cross-departmental planning for a 15+ engineer org, aligning technical strategy with company goals while fostering accountability, communication, and continuous learning.

Full Stack AI Engineer - VYNYL

(2021-03 - 2024-05)

• Designed, trained, and deployed ML and deep learning models (TensorFlow, PyTorch, Scikit-Learn) for NLP and computer vision use cases, improving accuracy 18% and cutting inference latency 30% from prototype to production.
• Built RAG and LLM-based applications using vector databases, LoRA/PEFT fine-tuning, and prompt engineering techniques, lifting response relevance 25% and cutting hallucination rates 40%.
• Developed Big Data pipelines and feature engineering workflows using Python, Pandas, NumPy, and Apache Spark, and built ETL pipelines with Airflow/dbt from Snowflake and PostgreSQL, processing 5TB+ daily while cutting runtime 35%.
• Collaborated with product, data, DevOps, and backend teams to integrate ML models into cloud-based production systems on AWS and GCP, using Docker, Kubernetes, and Java/Kafka event-driven microservices supporting 1M+ users.
• Applied model evaluation practices and instituted automated monitoring/alerting for model performance and data drift using MLflow, cutting degradation incidents 45% and response time 50%.
• Conducted research on new modeling approaches, including multimodal modeling and time series forecasting with XGBoost, and explored Rust-based, cache-optimized model-serving components, cutting inference latency 28%.
• Documented model architectures and training procedures in Git repositories, authored and reviewed 200+ pull requests via structured GitHub code review while mentoring 4 junior engineers and ensuring HIPAA-compliant, IAM-secured data handling.

Software Engineer/Machine Learning Engineer - Coderio

(2019-06 - 2021-02)

• Contributed to machine learning and computer vision model development, data preprocessing, training, and evaluation using Python and OpenCV, improving accuracy 15%, and built SQL/NoSQL data pipelines for web scraping and dataset prep.
• Assisted in deploying ML models and supporting microservices using Docker and AWS, gaining hands-on REST API design, and supported migration of ML services to containerized deployment, cutting deployment time 40%.
• Wrote unit and integration tests for ML-supporting services, raising test coverage to 85%, and collaborated with seniors to debug and optimize training/inference performance, implementing caching that cut latency 20%.
• Participated in GitHub code reviews and contributed to internal documentation for ML workflows and onboarding materials, using Git and Agile/Scrum practices to consistently deliver 90%+ of sprint commitments on time.
• Supported experimentation with predictive analytics models, explored IoT data streams and time-series analysis, and tracked/reported key metrics that contributed to a 12% improvement in forecast accuracy.
• Practiced adaptability and continuous learning while ramping up on new frameworks and tools, coordinating with teammates via Slack and GitHub to prioritize tasks and communicate progress on ML initiatives in a fast-paced environment.

Master of Science - Computer Science - FAST NUCES (2016-10 - 2018-09)