We are seeking an experienced and dynamic MLOps Lead to join our team. In this role, you will be responsible for leading the design, implementation, and management of our MLOps infrastructure, ensuring seamless deployment and operation of machine learning models in a cloud-native environment. 

As a Vice President, Applied LLMOps Lead work closely with data scientists, engineers, and IT teams to build and maintain scalable solutions that leverage the latest technologies, including LLMOps, Multi-GPU processing, Ray, and vLLM.

Job Responsibilities:

Lead the development and implementation of MLOps strategies and best practices to support the deployment and management of machine learning models at scale. Design and build scalable, efficient, and secure MLOps pipelines using the latest technologies, including LLMOps, Multi-GPU processing, Ray, and vLLM. Oversee the deployment and orchestration of machine learning models on AWS cloud platforms, specifically EKS and ECS. Collaborate with data scientists and engineering teams to ensure seamless integration of models into production environments. Implement monitoring, logging, and alerting solutions to ensure the reliability and performance of deployed models. Drive continuous improvement of MLOps processes, tools, and technologies to enhance efficiency and scalability. Provide technical leadership and mentorship to a team of MLOps engineers, fostering a culture of innovation and excellence. Stay up-to-date with the latest advancements in MLOps, cloud computing, and machine learning technologies, and evaluate their applicability to our operations. Ensure compliance with industry standards and regulations related to data security and privacy.

Required qualifications, capabilities and skills

10+ years working experience as an MLOps Lead Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience in MLOps, DevOps, or a related role, with a focus on deploying machine learning models at scale. Strong expertise in cloud platforms, particularly AWS, and experience with EKS and ECS. Proficiency in using MLOps tools and frameworks, including LLMOps, Ray, and vLLM. Experience with Multi-GPU processing and distributed computing. Solid understanding of containerization technologies such as Docker and Kubernetes. Strong programming skills in languages such as Python, with experience in building and deploying machine learning models.

Preferred qualifications, capabilities and skills:

Experience in the banking or financial services industry. Familiarity with data privacy and security regulations in the financial sector. Certifications in AWS or related cloud technologies. Excellent problem-solving skills and the ability to work collaboratively in a fast-paced, dynamic environment. Strong communication and leadership skills, with the ability to effectively manage and mentor a team.