DevOps / MLOps Engineer

Summary / Role Purpose

Join the Ansys Customer Excellence team and collaborate with our customers to pioneer the future of engineering. As an Application Engineer, you’ll address tangible engineering challenges, seamlessly integrate custom Ansys software into customer design workflows, and contribute to the growth of Ansys. Utilize your advanced technical expertise to design, develop, and deploy software applications for both on-premises and cloud environments using cutting-edge containerization technologies.

Key Duties and Responsibilities

• Workflow Development and Deployment:

Responsible for designing, developing, and deploying automation workflows using PyAnsys.

• Containerization:

Work with technologies such as Docker and Kubernetes to containerize applications. This includes creating Dockerfiles, managing Docker images, creating Kubernetes deployments, managing clusters, etc.

• Continuous Integration and Continuous Deployment (CI/CD):

Implement, manage, and optimize CI/CD pipelines to automate testing and deployment of applications.

• Documentation:

Create detailed technical documentation and guidelines to guide the end-users and to help with the maintenance.

• Technical Support and Training:

Provide technical support and training to both end-users and junior team members around containerized applications, as necessary.

Minimum Education/Certification Requirements and Experience

• BS in Computer Science, Information Technology, Mathematics or
• Quantitative Sciences with following respective years of experience. BS+2, MS
• Commercial experience with Docker images creation and optimization.
• Experience deploying and managing containerized applications in on-prem and/or cloud environments using Kubernetes.
• Solid understanding of DevOps methodologies.

Preferred Qualifications and Skills 

• Excellent technical knowledge and commercial experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong problem-solving skills and the ability to troubleshoot complex issues in cloud environments.
• Experience with machine learning frameworks and python libraries.
• Understanding of machine learning model lifecycle management, from development to deployment and scaling.
• Ability to monitor and maintain ML models in production environments to ensure performance and reliability.
• Certifications in relevant cloud technologies such as AWS Certified Developer, Azure Developer Associate, or Google Cloud Certified.
• Proven experience with Ansys software and PyAnsys.
• Ability to multitask and manage multiple tasks in a fast-paced environment.
• Excellent verbal and written English communication skills.
• Ability to travel as required.