Senior Machine Learning Engineer | MLOps & Scalable Systems
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Date: Jun 2, 2025
Location: PHOENIX, AZ, US, 85004-3903
Company: APS
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.
Summary
Senior Machine Learning Engineer | MLOps & Scalable Systems
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 machine learning systems in a complex enterprise environment. You'll own the full MLOps lifecycle-from prototyping to monitoring-and architect solutions that power intelligent, real-time decision-making across critical business functions.
This is a high-visibility role where you'll collaborate with cross-functional teams, influence architecture, and help define best practices that shape the future of ML at scale.
What You'll Do:
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 pipelines and streaming environments.
Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deep domain expertise in ML model design and infrastructure.
Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists to deliver real-world business value.
Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, including CI/CD, monitoring, testing, and documentation.
You'll Be a Great Fit If You Have:
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 to design scalable ML architectures for streaming and batch use cases.
A mindset for mentorship and technical leadership, with the ability to guide teams on best practices in production ML.
Minimum Requirements
Senior Machine Learning Engineer | MLOps & Scalable Systems
BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field
AND minimum six (6) years directly related data analytics, data science, predictive modeling, building and deploying machine learning solutions
OR advanced degree and four (4) 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.
High level of proficiency in commonly used programming languages and tools like Python, SQL, and cloud solutions to build and deploy scalable solutions.
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.
Preferred Special Skills, Knowledge or Qualifications
Masters or Doctorate degrees in relevant fields
2+ yrs of hands-on experience with major cloud machine learning and MLOps services in an enterprise setting.
Familiarity with PyTorch or Tensorflow.
1+ yr of experience with scaling infra using GPUs or PySpark
2+ yrs experience with MLOps services including docker, CI/CD, Kubernetes, and building/managing/monitoring pipelines.
Experience with serving generative AI services
Experience in integrating ML inferences with webservices
Major Accountabilities
1) Collaboration with customers and partners:Consult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.
Work closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.
Identify available and relevant data and the data sources.
Collaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.
Gather data, formulate cluster or nodes and establish performance checks on the large data models.
Design and implementation of solutions including data acquisition, storage, transformation, and analysis
Design, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.
Design models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.
Modeling complex problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and visualization techniques using existing or new front-end reporting & analytics tools.
Play key role in turning data into critical information and knowledge that can be used to make sound organizational decisions.
Propose innovative ways to look at problems by using data mining approaches and validate findings using experimental and iterative approaches.
Understand data transforming platforms and technologies and maintain a knowledge of discipline maturity.
Present findings to the business in a way that can be easily understood by business counterparts.
Make recommendations based on business requirements and knowledge of industry best practices.
Make technical decisions on advanced analytics initiatives.
Utilize programming language, such as R, Python, SQL, .net, Java or C++ to evoke the data from data source and model
Familiarity with Cloud structure and building, utilizing cloud technologies
Performing data acquisition using JSon, SQL, ODBC, JScript, or API for Big Data extracts
Transform and utilize streaming data with programming languages such as: KAFKA, SQL, Spark, and/or Azure
Export Compliance / EEO Statement
This position may require access to and/or use of information subject to control under the Department of Energy's Part 810 Regulations (10 CFR Part 810), the Export Administration Regulations (EAR) (15 CFR Parts 730 through 774), or the International Traffic in Arms Regulations (ITAR) (22 CFR Chapter I, Subchapter M Part 120) (collectively, 'U.S. Export Control Laws'). Therefore, some positions may require applicants to be a U.S. person, which is defined as a U.S. Citizen, a U.S. Lawful Permanent Resident (i.e. 'Green Card Holder'), a Political Asylee, or a Refugee under the U.S. Export Control Laws. All applicants will be required to confirm their U.S. person or non-US person status. All information collected in this regard will only be used to ensure compliance with U.S. Export Control Laws, and will be used in full compliance with all applicable laws prohibiting discrimination on the basis of national origin and other factors. For positions at Palo Verde Nuclear Generating Stations (PVNGS) all openings will require applicants to be a U.S. person.
Pinnacle West Capital Corporation and its subsidiaries and affiliates ('Pinnacle West') maintain a continuing policy of nondiscrimination in employment. It is our policy to provide equal opportunity in all phases of the employment process and in compliance with applicable federal, state, and local laws and regulations. This policy of nondiscrimination shall include, but not be limited to, recruiting, hiring, promoting, compensating, reassigning, demoting, transferring, laying off, recalling, terminating employment, and training for all positions without regard to race, color, religion, disability, age, national origin, gender, gender identity, sexual orientation, marital status, protected veteran status, or any other classification or characteristic protected by law.
For more information on applicable equal employment regulations, please refer to EEO is the Law poster. Federal law requires all employers to verify the identity and employment eligibility of every person hired to work in the United States, refer to E-Verify poster. View the employee rights and responsibilities under the Family and Medical Leave Act (FMLA). Arizona Public Service is a smoke free workplace.
Home based: Home based employees primarily work from their home offices and come into an APS facility on an as-needed basis.
*Employees are expected to reside in Arizona (or New Mexico for Four Corners-based employees).
*Working from a home office requires adequate technology and an appropriate ergonomic set up.
*Role types are subject to change based on business need.