About The Position

Our present and future success depends on the creative and dedicated people of our company who demonstrate the principles outlined in the APS Promise: Design for Tomorrow, Empower Each Other and Succeed Together. Are you a senior-level Machine Learning Engineer ready to make a big impact at scale? We're looking for a highly skilled ML Engineer to lead the design and deployment of production-grade machinelearning systems in a complex enterprise environment. You’ll own the full MLOps lifecycle—from prototyping tomonitoring—and architect solutions that power intelligent, real-time decision-making across critical businessfunctions.This is a high-visibility role where you’ll collaborate with cross-functional teams, influence architecture, and helpdefine best practices that shape the future of ML at scale.

Requirements

  • Held a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.
  • Deep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g.,Kubeflow, SageMaker, MLflow).
  • Proven ability todesign scalable ML architecturesfor streaming and batch use cases.
  • A mindset formentorship and technical leadership, with the ability to guide teams on best practices inproduction ML.
  • BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field
  • PLUS minimum four(4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role
  • OR advanced degree and two (2) years directly related experience. Possesses a combination of strong analytical and problem-solving skills and programming knowledge, or an equivalent combination of education and experience with demonstrated comparable knowledge and abilities.

Nice To Haves

  • Masters or Doctorate degrees in related fields.
  • Knowledge/experience in utility industry and business functions.
  • Certification in Data Science and/or predictive analytics
  • A high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.
  • Strong communication, presentation and writing skills.
  • Must be able to lead teams in evaluations and implementation of solutions.
  • Must be able to work with key internal and external stakeholders and all levels of management.

Responsibilities

  • Lead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable,reliable, and secure.
  • Architect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelinesand streaming environments.
  • Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deepdomain expertise in ML model design and infrastructure.
  • Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists todeliver real-world business value.
  • Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, includingCI/CD, monitoring, testing, and documentation.
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