DevOps Engineer

Job Description Summary

The DevOps Engineer will be responsible for implementing our AI/ML initiatives across product development and business productivity improvements. This role focus on MLOps, infrastructure, cloud technologies, and Generative AI tools, collaborating across corporate teams and business-specific stakeholders to ensure seamless alignment with organizational objectives.

Job Description

Major Responsibilities:

• Implement DevOps best practices to optimize software development and deployment processes and update those best practices documentation.
• Design, deploy, and manage scalable Cloud infrastructure using AWS or Azure platforms.
• Support the implementation and tracking of DevOps maturity to drive continuous improvement.
• Apply Agile process knowledge to enhance workflow management and team collaboration.
• Collaborate with development teams to enhance the CI/CD pipeline and cloud-based solutions.
• Design, and implement AI/ML infrastructure on cloud platforms (such as AWS, Azure), ensuring scalability, security, reliability and optimization for ML workloads.
• Design, and build MLOps pipelines including model training, validation, and deployment using industry-standard tools and platforms.
• Collaborate with data scientists/software engineers to implement AI/ML workflows.
• Implement security best practices for AI/ML infrastructure and data management at scale.
• Evaluate technologies by Analysis of Alternatives and implement Proof of Concepts to quickly validate before implementation.

• Bachelor's or master's degree in computer science, Engineering, or a related field with minimum of 4+ years hands-on experience in DevOps and MLOps roles.
• Hands-on experience in scripting languages such as Shell, Perl, or Python.
• Hands on experience in AWS or Azure Cloud platforms
• Experience with AWS-specific tools such as SageMaker, Cloud Formation
• Familiarity with open-source MLOps tools such as MLflow, Kubeflow, or TensorFlow, extended (TFX) for managing ML workflows and model lifecycles.
• Experience with Generative AI tools and frameworks.
• Knowledge of AI/ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn. Understanding of data management and ETL processes as well as handling large and diverse datasets.
• Excellent communication, interpersonal and problem-solving skills and ability to work independently and collaboratively.
• Adaptability to new technologies and methodologies.

• Certifications in relevant technologies or platforms.
• Experience in the Energy or Industrial IoT sectors.
• Embrace the GE Vernova Way of Working principles:

• Innovation: Contribute to creative solutions and advancements in technology.
• Customers: Focus on delivering outstanding value and service.
• Lean: Apply efficient processes to maximize productivity.
• One Team: Collaborate effectively across diverse teams and disciplines.
• Accountability: Take responsibility for achieving excellence in all tasks and projects.