{"schemaVersion":"jobsearcher.job.v1","id":"be41ea6f93ad3654bbd0cc08","url":"https://jobsearcher.com/jobs/be41ea6f93ad3654bbd0cc08","canonicalUrl":"https://jobsearcher.com/jobs/be41ea6f93ad3654bbd0cc08","title":"Machine Learning Engineer | MLOps & Scalable Systems","description":"Date: Apr 8, 2026\nLocation: PHOENIX, AZ, US, 85004-3903\nCompany: APS\nOur 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.\nSummary\nMachine Learning Engineer | MLOps & Scalable Systems\n\nAre 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.\nThis 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.\n\nWhat You’ll Do:\nLead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable,reliable, and secure.\nArchitect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelinesand streaming environments.\nAdvise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deepdomain expertise in ML model design and infrastructure.\nCollaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists todeliver real-world business value.\nOwn Quality & Governance: Establish and maintain best practices for ML lifecycle management, includingCI/CD, monitoring, testing, and documentation.\n\nYou’ll Be a Great Fit If You Have:\nHeld a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.\nDeep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g.,Kubeflow, SageMaker, MLflow).\nProven ability todesign scalable ML architecturesfor streaming and batch use cases.\nA mindset formentorship and technical leadership, with the ability to guide teams on best practices inproduction ML.\nSponsorship for U.S. work authorization is not available for this position, now or in the future.\nMinimum Requirements\nMachine Learning Engineer | Information Technology\nBS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field\nPLUS minimum four(4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role\nOR 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.\nMachine Learning Engineer, Senior | Information Technology\nBS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field\nPLUS minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role\nOR 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.\n\nPreferred Special Skills, Knowledge or Qualifications:\nMasters or Doctorate degrees in related fields.\nKnowledge/experience in utility industry and business functions.\nCertification in Data Science and/or predictive analytics\nA high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.\nStrong communication, presentation and writing skills.\nMust be able to lead teams in evaluations and implementation of solutions.\nMust be able to work with key internal and external stakeholders and all levels of management.\nMajor Accountabilities\n1) Collaboration with customers and partners:\n\nConsult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.\nWork closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.\n2) Data requirements and preparation:\n\nIdentify available and relevant data and the data sources.\nCollaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.\nGather data, formulate cluster or nodes and establish performance checks on the large data models.\nDesign and implementation of solutions including data acquisition, storage, transformation, and analysis\n3) Modeling and Deployment:\n\nDesign, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.\nDesign models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.\nModeling 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.\nPlay key role in turning data into critical information and knowledge that can be used to make sound organizational decisions.\nPropose innovative ways to look at problems by using data mining approaches and validate findings using experimental and iterative approaches.\nUnderstand data transforming platforms and technologies and maintain a knowledge of discipline maturity.\n4) Present results, provide recommendations and lead analytics efforts:\n\nPresent findings to the business in a way that can be easily understood by business counterparts.\nMake recommendations based on business requirements and knowledge of industry best practices.\nMake technical decisions on advanced analytics initiatives.\n5) Programming and Coding\n\nUtilize programming language, such as R, Python, SQL, .net, Java or C++ to evoke the data from data source and model\nFamiliarity with Cloud structure and building, utilizing cloud technologies\nPerforming data acquisition using JSon, SQL, ODBC, JScript, or API for Big Data extracts\nTransform and utilize streaming data with programming languages such as: KAFKA, SQL, Spark, and/or Azure\n6) Mentoring and coaching junior staff as necessary\n\nExport Compliance / EEO Statement\nThis 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.\n\nPinnacle 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.\n\nFor 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.\nHome based: Home based employees primarily work from their home offices and come into an APS facility on an as-needed basis.\nEmployees are expected to reside in Arizona (or New Mexico for Four Corners-based employees).\nWorking from a home office requires adequate technology and an appropriate ergonomic set up.\nRole types are subject to change based on business need.\n\nJob Segment: Nuclear, Energy","company":"Aps","rawCompany":"aps","city":"Phoenix","state":"AZ","isRemote":false,"isActive":false,"createdAt":"2026-05-05T07:06:42.959Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer | MLOps & Scalable Systems","description":"Date: Apr 8, 2026\nLocation: PHOENIX, AZ, US, 85004-3903\nCompany: APS\nOur 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.\nSummary\nMachine Learning Engineer | MLOps & Scalable Systems\n\nAre 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.\nThis 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.\n\nWhat You’ll Do:\nLead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable,reliable, and secure.\nArchitect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelinesand streaming environments.\nAdvise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deepdomain expertise in ML model design and infrastructure.\nCollaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists todeliver real-world business value.\nOwn Quality & Governance: Establish and maintain best practices for ML lifecycle management, includingCI/CD, monitoring, testing, and documentation.\n\nYou’ll Be a Great Fit If You Have:\nHeld a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.\nDeep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g.,Kubeflow, SageMaker, MLflow).\nProven ability todesign scalable ML architecturesfor streaming and batch use cases.\nA mindset formentorship and technical leadership, with the ability to guide teams on best practices inproduction ML.\nSponsorship for U.S. work authorization is not available for this position, now or in the future.\nMinimum Requirements\nMachine Learning Engineer | Information Technology\nBS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field\nPLUS minimum four(4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role\nOR 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.\nMachine Learning Engineer, Senior | Information Technology\nBS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field\nPLUS minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role\nOR 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.\n\nPreferred Special Skills, Knowledge or Qualifications:\nMasters or Doctorate degrees in related fields.\nKnowledge/experience in utility industry and business functions.\nCertification in Data Science and/or predictive analytics\nA high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.\nStrong communication, presentation and writing skills.\nMust be able to lead teams in evaluations and implementation of solutions.\nMust be able to work with key internal and external stakeholders and all levels of management.\nMajor Accountabilities\n1) Collaboration with customers and partners:\n\nConsult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.\nWork closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.\n2) Data requirements and preparation:\n\nIdentify available and relevant data and the data sources.\nCollaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.\nGather data, formulate cluster or nodes and establish performance checks on the large data models.\nDesign and implementation of solutions including data acquisition, storage, transformation, and analysis\n3) Modeling and Deployment:\n\nDesign, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.\nDesign models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.\nModeling 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.\nPlay key role in turning data into critical information and knowledge that can be used to make sound organizational decisions.\nPropose innovative ways to look at problems by using data mining approaches and validate findings using experimental and iterative approaches.\nUnderstand data transforming platforms and technologies and maintain a knowledge of discipline maturity.\n4) Present results, provide recommendations and lead analytics efforts:\n\nPresent findings to the business in a way that can be easily understood by business counterparts.\nMake recommendations based on business requirements and knowledge of industry best practices.\nMake technical decisions on advanced analytics initiatives.\n5) Programming and Coding\n\nUtilize programming language, such as R, Python, SQL, .net, Java or C++ to evoke the data from data source and model\nFamiliarity with Cloud structure and building, utilizing cloud technologies\nPerforming data acquisition using JSon, SQL, ODBC, JScript, or API for Big Data extracts\nTransform and utilize streaming data with programming languages such as: KAFKA, SQL, Spark, and/or Azure\n6) Mentoring and coaching junior staff as necessary\n\nExport Compliance / EEO Statement\nThis 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.\n\nPinnacle 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.\n\nFor 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.\nHome based: Home based employees primarily work from their home offices and come into an APS facility on an as-needed basis.\nEmployees are expected to reside in Arizona (or New Mexico for Four Corners-based employees).\nWorking from a home office requires adequate technology and an appropriate ergonomic set up.\nRole types are subject to change based on business need.\n\nJob Segment: Nuclear, Energy","datePosted":"2026-05-05T07:06:42.959Z","dateModified":"2026-05-05T07:06:42.959Z","hiringOrganization":{"@type":"Organization","name":"Aps","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Phoenix","addressRegion":"AZ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"be41ea6f93ad3654bbd0cc08"},"url":"https://jobsearcher.com/jobs/be41ea6f93ad3654bbd0cc08"}}