Manager, AI

Company Alaska Airlines The Team Guided by our purpose, core values, and leadership principles, we are creating an airline people love. Our corporate teams set the strategies and operational plans to ensure the success of our company. Whether we use our expertise in accounting, human resources, finance, planning, legal, marketing, or any of our operational divisions, our shared passion for travel and our guests is what motivates us to achieve excellence each day. If you share our passion for creating an airline people love, we want to hear from you.

Summary The Manager, AI is responsible for executing strategy for the development and deployment of AI and machine learning solutions, including Generative AI (GenAI), to optimize Alaska Airlines' performance across system operations, flight operations, inflight services, maintenance and engineering, airport operations, scheduling, network planning, marketing, and revenue management at Alaska Air Group (AAG).  As a people leader, this role provides technical leadership, is accountable for the team’s delivery of AI solutions, develops and grows the team, and collaborates with cross-functional stakeholders to prioritize and deliver scalable, efficient AI solutions that drive the greatest business impact.

Lead the development and deployment of AI and machine learning models, including GenAI-powered applications, to enhance operational efficiency and revenue optimization.  Oversee the full AI/ML software development lifecycle, ensuring best practices in model training, evaluation, deployment, and monitoring.  Accountable for the team’s delivery of responsible, reliable AI and ML solutions in production.  Develop and integrate GenAI models for text generation, summarization, conversational AI, and content creation to support automation and decision-making processes.  Collaborate with Data Engineering, MLOps, ITS, and business stakeholders to integrate AI and GenAI solutions into enterprise systems.  Ensure best practices in model deployment, monitoring, and performance optimization.  Establish scalable AI frameworks and reusable model architectures to accelerate solution deployment.  Guide the adoption of MLOps practices for both traditional and GenAI model deployment, versioning, monitoring, and retraining.  Stay current with advancements in LLMs (Large Language Models), agentic and multimodal AI, and transformer-based architectures to assess their applicability for business use cases.  Mentor and develop the team through performance management, ongoing feedback, and stretch assignments, fostering an inclusive culture of innovation, continuous learning, and collaboration.  Present AI and GenAI models and findings to stakeholders, ensuring clear communication of technical concepts.  Job-Specific Experience, Education & Skills Required   5 years of experience in AI, machine learning, or data science, with a proven track record of deploying AI solutions at scale.  2 years of leadership experience.  Bachelor’s degree in a relevant field, or an additional 2 years of training/experience in lieu of this degree.  Proficiency in Python and experience with AI/ML libraries such as TensorFlow, PyTorch, Scikit-Learn, and XGBoost.  Strong expertise in machine learning, deep learning, and Generative AI (e.g., GPT, BERT, DALL·E, diffusion models, etc.).  Hands-on experience with cloud platforms (Azure, AWS, or GCP) and AI/ML deployment tools.  Experience with LLM fine-tuning, retrieval-augmented generation (RAG), and prompt engineering.  Experience with MLOps practices, including CI/CD for machine learning, model monitoring, and automated retraining.  Proficiency in Databricks for large-scale data processing and model development.  Strong understanding of data engineering concepts, including data pipelines, ETL, and feature engineering.  Excellent problem-solving skills and ability to translate business