DUNCAN McKINNON
Forward Deployed AI Engineer
Seattle, WA
PROFESSIONAL SUMMARY
Forward Deployed AI Engineer at Pydantic with experience spanning software engineering, data science, MLOps, and LLM applications. I work with teams to build reliable agentic systems using Pydantic AI and Logfire, applying full-stack observability and continuous improvement patterns to production AI. My prior work includes machine learning infrastructure, reinforcement learning solutions, and recommendation systems driving $40M+ in revenue.
PROFESSIONAL EXPERIENCE
Pydantic
Forward Deployed AI Engineer
August 2026 – Present | Seattle, WA
- Develop solutions for clients using the Pydantic AI and Logfire product stack
- Collaborate with engineering teams to apply full-stack observability and continuous improvement patterns to agentic systems
- Develop opinionated guidance through technical blogs and workshops for the developer community using Pydantic tools
Arize AI
November 2023 – August 2026 | Seattle, WA
Forward Deployed AI Engineer
March 2025 – August 2026
- Develop safe, scalable LLM workflows and agent systems for production use cases in public and private sectors as the first field AI engineer
- Partner with enterprise AI and ML infrastructure teams to solve complex challenges in AI evaluation, MLOps, production workflow automation, observability, and monitoring
- Develop and maintain public Python SDK that accelerates integration of observability and evaluation tools in production environments
- Create frameworks and reusable project templates to increase velocity in customer engagements, collaborating across engineering, product, solutions, and GTM teams
AI Solutions Engineer
November 2023 – March 2025
- Built MLOps observability integrations for real-time and batch workflows on the Instacart Griffin ML platform
- Collaborated with leadership and data science teams across 15+ enterprise and public sector accounts to integrate model inferences and identify actionable, value-driving insights
- Instrumented LLM tracing and monitoring for enterprise clients, providing guidance on GenAI production evaluations, guardrails, prompting, observability, and experimentation workflows
Wayfair
Machine Learning Engineer 2
March 2022 – November 2023 | Seattle, WA
- Led engineering team in implementing solutions to reduce latency in real-time machine learning systems
- Designed and tested customer recommendation systems driving over $40M/year in revenue
- Stress tested GCP feature serving platform with various models, gaining buy-in for recommended improvements to online ML systems
- Developed MLflow model deployment and REST service tools that reduced time to productionalize models from days to hours
- Implemented monitoring capabilities to track machine learning performance and data drift using Arize platform
Microsoft (via Neal Analytics)
Data Scientist
July 2020 – March 2022 | Seattle, WA
- Led data science consulting team in developing resource allocation forecast models and pipelines that reduced infrastructure failure response times by 5-7 minutes for major national utility provider
- Delivered end-to-end deep reinforcement learning solution for international mining company, developing orchestration framework to manage simulation, training, assessment, and visualization
- Built web API to generate strategic planning reports using Python, AnyLogic, Azure, Flask, Docker, and Microsoft Project Bonsai
- Led implementation of scalable end-to-end retail demand forecasting solutions for multiple clients using Azure Databricks, Apache Spark, and Prophet
AT&T
January 2018 – July 2020 | Seattle, WA
Software Engineer 2
June 2019 – July 2020
- Designed, developed, and deployed personalization services supporting 20+ million unique requests daily across 30+ customer webpages on 3 independent applications
- Led research and presented comparative cloud architecture strategies for migrating on-premise services to AWS and Azure
- Researched and implemented online machine learning system capturing real-time importance rankings to improve content recommendations
Software Engineer 1
January 2018 – June 2019
- Created microservices for configurable content recommendation system
- Led creation of application monitoring systems to flag malicious traffic, identify service inefficiencies, and present performance results to business leadership
EDUCATION
Master of Science in Applied & Computational Mathematics & Statistics (Data Science)
University of Notre Dame
2018 – 2020
Bachelor of Science in Computer Engineering
University of Washington Bothell
2014 – 2017
Minors: Mathematics & Physics
PUBLICATIONS
- The AI FDE Dilemma: Overpriced Engineers or Catalysts of Industry Change? (LinkedIn, August 2026)
- New Control Paradigms with Agentic Components (LinkedIn, July 2026)
- How to measure AI productivity: From LLM token costs to business value with Arize AX (Arize AI, July 2026)
- Coding agent tracing and evaluation: An open source tool to improve AI coding workflows (Arize AI, May 2026)
- Adaptive Guardrails for Agentic Applications (LinkedIn, 2026)
- Great Books for AI Engineering (Towards Data Science, 2025)
- Achieving Self-Service Onboarding for MLOps Tooling (Wayfair Tech Blog, 2023)
- Great Books for Data Science 2 (Towards Data Science, 2021)
CERTIFICATIONS
- Reinforcement Learning Specialization (University of Alberta, 2021)
- Generative Adversarial Networks Specialization (DeepLearning.AI, 2021)
- Natural Language Processing Specialization (DeepLearning.AI, 2020)
- Deep Learning Specialization (DeepLearning.AI, 2018)