Principal AI Engineer

Key Responsibilities

• Define and execute NOIRLab's enterprise AI strategy, technical vision, and multi-year roadmap aligned with scientific, engineering, and operational goals.
• Identify, evaluate, and prioritize high-value AI use cases that deliver measurable business and scientific impact.
• Establish AI engineering standards, development methodologies, reusable frameworks, and best practices that support scalable adoption across the organization.
• Audit and implement emerging technologies, including generative AI, foundation models, LLMs, AI agents, and advanced machine learning approaches.
• Develop governance frameworks, model evaluation processes, and lifecycle management practices that ensure responsible, secure, and effective AI deployment.
• Develop key performance indicators (KPIs) and success metrics to evaluate the effectiveness of AI initiatives.

AI Platform Architecture & Solution Development

• Design and implement secure, scalable, and production-ready AI platforms and applications that integrate with scientific research systems, software platforms, and operational workflows.
• Lead the development of LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, copilots, and other advanced AI solutions.
• Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement.
• Partner with IT and enterprise technology teams to ensure AI systems meet organizational requirements for reliability, scalability, security, and compliance.

Advanced Analytics & Scientific Computing

• Develop advanced analytics capabilities that support scientific discovery, operational decision-making, and organizational effectiveness.
• Apply machine learning, statistical modeling, and AI techniques to large-scale scientific datasets and computational workflows.
• Evaluate and optimize model performance, accuracy, explainability, and operational effectiveness.
• Collaborate with researchers and technical teams to leverage AI in solving complex scientific and engineering challenges.

Technical Leadership & Cross-Functional Collaboration

• Serve as the organization's senior technical authority for AI, machine learning, and advanced analytics.
• Mentor engineers, scientists, and technical teams in AI technologies, architecture, and responsible AI practices.
• Collaborate across scientific, engineering, and operations organizations to drive AI adoption and innovation.
• Engage with external research institutions, observatories, and scientific communities to promote collaboration, interoperability, and knowledge sharing.
• Foster a culture of experimentation, continuous improvement, and responsible innovation.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical field, or equivalent combination of education and experience.
• 15+ years of experience in software engineering, artificial intelligence, machine learning, or related fields, including experience serving as a Principal Engineer, AI Technical Lead, Architect, or equivalent senior technical leadership role.
• Demonstrated success designing, building, and deploying production-scale AI and machine learning solutions.
• Deep expertise in machine learning, generative AI, large language models, modern AI architectures, and AI engineering best practices.
• Experience developing and deploying LLM applications, RAG systems, AI agents, and foundation model-based solutions.
• Strong background in cloud-native architecture, distributed systems, data platforms, and AI infrastructure.
• Experience implementing AI operationalization practices, including model deployment, monitoring, evaluation, governance, and lifecycle management.
• Expertise in secure, responsible AI development and model governance frameworks.
• Advanced software engineering skills and proficiency with modern programming languages, frameworks, and AI development ecosystems.
• Exceptional communication, leadership, and collaboration skills with the ability to influence diverse technical and non-technical stakeholders.

Preferred Qualifications

• Experience applying AI, machine learning, or advanced analytics within scientific research environments.
• Familiarity with astronomical datasets, observatory operations, scientific instrumentation, or related research domains.
• Experience with high-performance computing (HPC), large-scale data processing, or scientific computing platforms.
• Contributions to open-source software, AI frameworks, or scientific computing communities.
• Experience building organizational AI capabilities and driving enterprise-wide AI adoption strategies.

Salary Range

$179,000-$226,000. The final salary will depend on skills, qualifications, experience and job location.