Job Description • We are looking for a highly skilled ML Ops Engineer to join our team. The ML Ops Engineer will be responsible for developing, deploying, and maintaining machine learning models and infrastructure. This role requires collaboration with various teams, including data science, engineering, and operations, to support and enhance our machine learning capabilities. • Abilities/Skill and Other Requirements – Exceptional Technical Skills are assumed • • • TOP: Self-starter with a ‘learn and apply’ attitude in an intense, fast-paced environment. • • TOP: Experience working with leading stakeholder groups, including technical, product, business, and vendor teams. • • TOP: Experience with ML Ops/AI tools and frameworks such as: Jupyter, Nvidia Global Catalog (NGC), MLFlow, and RunAI • • TOP: Experience with Docker, Slurm, Python, Conda • • Highly Desired: Experience with cloud platforms such as Azure, or Google Cloud. • • Highly Desired: Kubernetes • • Highly Desired: PyTorch/torchrun/TorchX • • 3rd-party partner management experience. • • Excellent communication, collaboration, and documentation skills. • • Proven track record of deploying and maintaining machine learning models in production. • • Ability to anticipate and manage infrastructure risks and issues with full transparency. • • Confident, solution-oriented independent worker. • • Must work independently while managing different sets of business, technology, and vendor stakeholders. • • Proven problem-solving and organizational skills. • • Experience with CI/CD pipelines and automation tools. • • Local to Cincinnati region will be given preference. • • Some travel to the Blue Ash Technical center may be required during critical deployments. • Key Responsibilities • • • Develop, deploy, and maintain machine learning models ensuring their reliability, performance, and scalability. • • Develop, deploy, and maintain machine learning tools. • • Automate ML workflows • • Monitor model performance and troubleshoot issues to ensure high availability and performance. • • Collaborate with data science, engineering, and operations teams to support and enhance the machine learning infrastructure. • • Implement and maintain security best practices for ML systems. • • Develop and maintain documentation for ML workflows, procedures, and processes. • • Manage infrastructure risk, develop mitigation plans, and escalate decisions and unresolved issues daily. • • Work with peers to develop and drive goals, define technical specifications, and detailed implementation plans for ML projects • • Effectively apply skills to impact ML infrastructure decisions. • Focus on the benefits to be realized and the outcomes to be achieved