AI/ML Ops Engineer

Key Skills

.NET, Aartificial intelligence, AWS, AWS Sagemaker, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data Engineering, DevOps, Docker, Infrastructure-as-code-IaC, Kubernetes-K8s, Machine learning techniques, MLOps tools, Python Programming

Job Description

The person on this position is a part of AICOE - Technical Platform team at Global Service Center. As an AI/ML Ops Engineer, you will be responsible for deploying, and managing generative AI solutions in production and non-production environment. You will work closely with data scientists, software engineers, and IT teams to ensure the smooth operation and scalability of AI solutions. This role requires a strong understanding of both AI/ML concepts and DevOps practices.

Duties and responsibilities:

• Design, implement, automate, and manage Generative AI solutions CI/CD pipelines and infrastructure.
• Deploy and monitor AI models in cloud environments.
• Collaborate with AI Engineers to optimize and scale AI models.
• Ensure the reliability, scalability, and performance of AI systems.
• Manage cloud-based AI/ML services, particularly in AWS or Azure.
• Implement and monitor security best practices for AI/ML environments.
• Troubleshoot and resolve issues related to AI/ML systems.
• Develop and maintain documentation for AI/ML workflows and processes.
• Provide guidance and support to other teams on AI/ML best practices and utilization.
• FinOps monitoring cloud resources and optimizing usage.

Skills and Expertise:

• Expertise in cloud platforms, particularly AWS or Azure.
• Strong knowledge of AI concepts.
• Proficiency in programming languages such as Python, .Net.
• Knowledge of containerization technologies (e.g., Docker, Kubernetes).
• Experience with CI/CD tools and practices like Github and Github Actions.
• Familiarity with infrastructure as code (IaC) tools such as BICEP, Terraform or CloudFormation.
• Strong problem-solving and troubleshooting skills.
• Excellent communication and collaboration abilities.

Minimum Qualifications:

• Experience with DevOps practices and tools.
• 3+ years of experience in cloud engineering or MLOps.
• Experience deploying and managing and providing support to cloud resources in production.
• Proficiency with cloud-based AI/ML services (AWS SageMaker, Azure ML, etc.).

Preferred Qualifications:

• Bachelor’s degree in computer science, Engineering, or a related field.
• Experience deploying and managing AI models in production.
• Experience with Generative AI frameworks and libraries.
• 5+ years of experience in DevOps/MLOps.
• Experience with AI/ML in a manufacturing or industrial context.
• Certification in AWS, Azure, or relevant AI/ML technologies.
• Knowledge of data engineering principles and practices.

Why Join Us:

• Opportunity to work with cutting-edge AI/ML technologies.
• Collaborative and innovative work environment.
• Professional development and growth opportunities.
• Competitive salary and benefits package.
• Contribution to impactful AI initiatives in a global company.

Learn more about 3M’s creative solutions to the world’s problems at www.3M.com or on Twitter @3M.

3M es un empleador que ofrece las mismas oportunidades. 3M no discriminará a ningún solicitante de empleo por razones de raza, color, edad, religión, sexo, orientación sexual, identidad o expresión de género, origen nacional, discapacidad o estado de veterano.

Our approach to flexibility is called Work Your Way, which puts employees first and drives well-being in ways that enable 3M’s business and performance goals. You have flexibility in where and when work gets done. It all depends on where and when you can do your best work.

Please note: your application may not be considered if you do not provide your education and work history, either by: 1) uploading a resume, or 2) entering the information into the application fields directly.