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Summary
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 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.
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 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.
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 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.
Sponsorship for U.S. work authorization is not available for this position, now or in the future.
Minimum Requirements
Machine Learning Engineer | Information Technology
- 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.
Machine Learning Engineer, Senior | Information Technology
- BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field
- PLUS minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role
- 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.
Preferred Special Skills, Knowledge or Qualifications:
- 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.
Major Accountabilities
- 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 experimenti