AI Engineer - Advisor Software

Rockwell Automation•Mayfield Heights, OH

6d•Hybrid

About The Position

As an AI engineer at Rockwell Automation, you will specialize in the implementation and deployment of AI systems at scale. You will focus on the technical infrastructure, optimization, and integration of AI systems into our existing Advisor Software framework. You will collaborate with data scientists, software engineers, and development teams to ensure smooth and efficient functioning of AI systems. You will have a broad range of skills that bridge the gap between AI development and software engineering. They combine their knowledge of AI algorithms and frameworks with expertise in software engineering principles and practices.

Requirements

Bachelor's Degree.
Legal authorization to work in the US is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening.

Nice To Haves

Typically requires 2+ years of relevant experience in software development.
Proficiency in AI programming languages, such as Python, Java, C#, or C++, is vital for AI engineers to develop and maintain software applications and systems.
Familiar with popular AI frameworks and libraries, such as TensorFlow or PyTorch, and understand how to leverage them for efficient model building and deployment.
Knowledge of cloud platforms, such as Amazon Web Services or Microsoft Azure, enables AI engineers to deploy and manage AI systems in scalable and cost-effective environments.
Have expertise in distributed computing frameworks to optimize large-scale data processing efficiently.
AI engineers design and implement effective data pipelines, data storage systems, and workflow automation to manage the flow of data
Ensure optimal performance of AI systems.

Responsibilities

Set up the technical infrastructure required for AI systems, including hardware, software, and data storage solutions.
Design and configure computing environments to support the execution of AI models.
Optimize AI models and algorithms for efficient execution and scalability.
Automate AI infrastructures for the data science team.
Leverage techniques such as model pruning, quantization, and parallel computing to reduce computational requirements and improve performance.
Integrate AI systems into existing software frameworks or platforms, ensuring compatibility and seamless interaction with other components.
Define APIs and protocols for communication between AI systems and external services.
Implement and maintain CI/CD (Continuous Integration and Continuous Delivery) pipelines to automate the deployment, testing, and monitoring of AI systems.
Ensure rapid and reliable software releases.
Monitor the performance and health of AI systems, detecting and addressing issues, such as system failures, performance degradation, or data inconsistencies.
Apply debugging and troubleshooting techniques to resolve technical problems.