Principal AI Engineer CPS Energy : Texas Category: Engineer

Position Summary

The Principal AI Engineer is responsible for leading the strategy design, development, and operationalization of Artificial Intelligence (AI) and Machine Learning (ML) initiatives across the organization. This individual will serve as the enterprise leader in advancing both traditional and Generative AI capabilities. Working under the direction of the Director of Data and Advanced Analytics, this individual will shape and execute a cohesive AI and GenAI strategy that drives business innovation, operational efficiency, and digital transformation.

This role requires collaboration across business units, technical teams, and external partners to convert complex problems into AI-enabled solutions that provide measurable value.

Grade: 20
Final date to receive applications:
Open until filled

• Lead the strategic vision and execution of enterprise-wide AI initiatives, ensuring alignment with organizational goals and digital transformation objectives.
• Collaborate with cross-functional teams to embed AI solutions into enterprise data workflows, ensuring models are well-governed, scalable, and deliver measurable business value by streamlining operations, improving decision-making, and unlocking actionable insights.
• Lead the design, development, and deployment of AI/ML solutions to support business functions such as forecasting, anomaly detection, computer vision, and natural language understanding.
• Lead the design, development and deployment of GenAI solutions using large language models (LLMs), image generation models, and retrieval-augmented generation (RAG) architectures for applications such as summarization, content generation, and intelligent automation.
• Analyze and communicate AI model results and accuracy in a way that is accessible to non-technical stakeholders, ensuring transparency and informed decision-making.
• Develop, lead, and continuously refine the organization’s Generative AI (GenAI) strategy, identifying high-impact use cases and enabling enterprise-wide adoption.
• Partner with internal teams to embed GenAI into business processes, tools, and platforms to improve decision-making, customer engagement, and operational workflows.
• Establish and maintain MLOps frameworks to ensure reproducibility, scalability, and continuous improvement of deployed models.
• Guide model governance and ethical AI practices, including bias mitigation, explainability, version control, and compliance with regulatory, privacy, and security requirements.
• Embed AI risk management via risk assessments, audits, and ensuring alignment with enterprise standards in partnership with internal audit, legal, and regulatory teams.
• Stay abreast of advancements in AI/ML and GenAI research and technology; introduce and assess emerging tools and techniques for potential adoption.
• Create and maintain reusable AI components, services, and APIs to accelerate solution development and delivery.
• Drive AI literacy and GenAI understanding across the organization through training, enablement programs, and cross-functional collaboration.
• Engage with external vendors, research institutions, and AI partners to integrate best-in-class AI capabilities and platforms.
• Provide technical leadership, mentorship, and guidance to AI-focused staff and other cross-functional team members.

Proven experience designing and implementing end-to-end AI/ML workflows using Python, Tensor Flow, PyTorch, Scikit-learn, or equivalent.

Demonstrated experience in supporting or driving execution of enterprise-wide AI initiatives in coordination with business and technical stakeholders.

Ability to influence direction and adoption of AI solutions through strong collaboration and delivery leadership.

Demonstrated ability to apply AI to structured and unstructured data sources, including text, images, and time series data.

Strong expertise in cloud-based machine learning platforms (e.g., Azure ML, AWS Sage Maker, Google Vertex AI).

Familiarity with data engineering and storage technologies including SQL, Spark, Delta Lake, and cloud-based data lakes.

Experience with MLOps practices, including automated…