Principal AI Engineer
Listed on 2026-06-18
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist, Data Engineering
Principal AI Engineer
Category: AI
Req
Date:
May 9, 2026
Location:
Gilbert, AZ, US 85297 Remote, US
As a Principal AI Engineer, you will be pivotal in driving the evolution of our AI-powered products and solutions, shaping the future of education through innovative technology. This position will lead the design, development and deployment of scalable solutions using Generative AI and Machine Learning foundational models. As a hands‑on technical lead, you will guide the team in solving complex AI problems, architecting solutions for AI projects, and contributing to the successful implementation of AI strategy Principal AI Engineer position will report to the Principal Architect and will work with the Innovation, Architecture, NASM and Ascend ITS teams, and coordinate with other teams that have a stake in the development process.
WHEREYOU’LL WORK
This position will work a hybrid schedule from our Gilbert, AZ office location. Remote will be considered.
HOW YOU’LL SPEND YOUR TIME- Lead the Design, develop, and deploy innovative, scalable AI/ML solutions focusing on the full lifecycle of projects from conception to deployment, including building and optimizing AI models
- Lead the team in building scalable, high-performance LLMOps pipelines.
- Design and co‑own the Generative AI/ML system and project architecture, frameworks, tools, and processes
- Remain versed on the latest advancements in the field, leading research initiatives, experimenting with new algorithms, and identifying opportunities for innovation. This can involve exploring LLMs (Large Language Models), embedding pipelines, and generative AI search/retrieval frameworks.
- Provide technical leadership, guidance, and hands‑on coding throughout the implementation phase, ensuring seamless integration of AI features, aligning with stakeholders to define AI and Product Roadmaps which enable corporate objectives
- Manage and preprocess large datasets, design efficient data processing pipelines, and ensure data integrity and security in adherence to privacy regulations.
- Experience designing and implementing AI/ML models with feedback loops and automated re‑training and fine‑tuning pipelines
- Collaborate with cross‑functional teams, including product teams, data scientists, and data engineers, software engineers to integrate AI capabilities into existing and new products
- Provide technical mentorship and guidance to team members, fostering a culture of growth and innovation
- Guide junior engineers in best practices for AI/ML, review their work, and offer feedback to foster their growth. Oversee and guide design review sessions across different projects, ensuring consistency and adherence to best practices.
- Ensure that AI solutions are developed and deployed ethically, considering fairness, accountability, and transparency
- Take ownership of defining project milestones, timelines, and ensuring successful delivery within specified deadlines
- Contribute to the development and maintenance of coding standards, best practices, and documentation
- Stay up to date with the latest AI Developments and trends
- Bachelor’s degree preferred in Computer Science/Artificial Intelligence or closely related field. Equivalent professional experience will also be considered. High School diploma or GED required.
- 6+ years of progressive work experience in data and analytics, big data related positions with at least 3 years in developing and implementing Analytical applications.
- Experience building and deploying LLM models, prompt engineering, context management, and embedding techniques using MS Azure and competitive cloud services.
- Advanced understanding and practical experience in machine learning and natural language processing, especially large language models (LLMs) and their integration, optimization, and evaluation.
- Experience using analytics/ML tools such as Python, NLTK, Hugging Face, SQL, and Snowflake.
- Experience with CI/CD pipelines, containerization technologies like Docker and Kubernetes, and MLflow or similar tools for lifecycle management of machine learning models.
- Proven ability to translate innovative ideas into practical, scalable solutions.
- Intrinsic…
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