AI Operations Engineer Resume Example

Lead AI Operations Engineer · EdgeFlow Technologies

Seattle, WA · 2022 - Present

• Led the design and implementation of MLOps pipelines for deploying computer vision models to industrial IoT devices, reducing model deployment time by 40% (from 2 days to 1.2 days).
• Developed custom monitoring frameworks for edge-deployed ML models, detecting inference drift with 95% accuracy and enabling proactive maintenance.
• Optimized inference performance of models on NVIDIA Jetson and ARM processors, achieving an average 25% reduction in latency and 30% lower power consumption.
• Managed a fleet of over 500 edge AI devices, implementing automated updates and rollback strategies that maintained 99.8% uptime.
• Mentored a team of 3 junior MLOps engineers, establishing best practices for containerization and orchestration of edge workloads.

MLOps Engineer, Embedded Systems · Connective AI Solutions

Redmond, WA · 2019 - 2022

• Engineered and maintained CI/CD pipelines for deploying machine learning models to automotive embedded systems, resulting in a 30% faster iteration cycle for model updates.
• Implemented model quantization and pruning techniques using TensorFlow Lite and OpenVINO, reducing model size by 60% and improving inference speed by 15% on target hardware.
• Developed automated testing suites for validating ML model performance and robustness on various embedded platforms, increasing test coverage by 70%.
• Contributed to the development of a custom, lightweight container orchestration system (similar to K3s) for managing ML workloads on vehicle ECUs, enhancing scalability for future features.
• Collaborated with data scientists and firmware engineers to integrate new ML features, improving sensor data processing efficiency by 20%.

M.S. in Electrical & Computer Engineering, Machine Learning Specialization · University of Washington

Seattle, WA · 2017 - 2019

• Thesis: 'Optimizing Quantized Neural Networks for Real-time Inference on Embedded Systems'
• GPA: 3.9/4.0, Dean's List

B.S. in Computer Science · Georgia Institute of Technology

Atlanta, GA · 2013 - 2017

• Graduated with High Honors
• Relevant coursework: Distributed Systems, Embedded Systems Programming, Machine Learning

AI Operations Engineer · CognitiveFlow AI

Seattle, WA · 2021 - Present

• Designed and implemented MLOps pipelines for critical AI models, accelerating model deployment cycles by 30% and enabling continuous integration.
• Built and maintained scalable ML infrastructure on AWS (SageMaker, EKS) supporting 10+ active machine learning models and 500k daily inferences.
• Established comprehensive monitoring and alerting systems for ML models, detecting performance degradations and data drift with 95% accuracy.
• Optimized model serving infrastructure, reducing inference latency by 25% and cutting operational costs by 15% through resource scaling strategies.
• Collaborated with data scientists to containerize ML applications using Docker and orchestrate with Kubernetes, streamlining development to production handoffs.

DevOps Engineer · Apex Cloud Solutions

Seattle, WA · 2018 - 2021

• Automated deployment processes for enterprise cloud applications, reducing deployment times by 40% and minimizing manual errors by 60%.
• Managed and optimized AWS infrastructure for high availability applications, improving system uptime to 99.99% across 5 production environments.
• Developed and maintained CI/CD pipelines using Jenkins and GitLab CI, supporting over 20 development teams and 150+ microservices.
• Implemented infrastructure as code (IaC) with Terraform, provisioning and managing over 100 AWS resources efficiently.

Bachelor of Science in Computer Science · University of Washington

Seattle, WA · 2014 - 2018

• Graduated Magna Cum Laude
• GPA: 3.8/4.0
• Coursework included Distributed Systems, Machine Learning Fundamentals, Cloud Computing