Senior Machine Learning Engineer | MLOps & Scalable Systems

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.

 

Why Join Us?

You’ll be part of a team that’s not just experimenting with ML, but embedding it into the core of our business—transforming the way we serve our customers and manage critical infrastructure. If you’re passionate about applied machine learning and want to build solutions that matter, we want to meet you.

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 cu