Job
Description
As a Subject Matter Expert - ML or LLM Ops , you will be a thought leader, guiding Organization MLOps and LLMOps strategy and overseeing the development of scalable infrastructure and processes. Be an Architect of Innovation: This is your opportunity to join a dynamic team and leverage your expertise as an MLOps & LLM Ops SME. You'll play a pivotal role in designing and implementing robust pipelines for our ML and LLM models, ensuring efficient deployment and continuous optimization. Responsibilities: - Provide strategic direction and technical leadership for MLOps and LLM Ops initiatives, and ensure alignment with business objectives. Demonstrate an in-depth understanding of MLOps and LLM Ops principles, including data pipelines, model training, deployment frameworks, and monitoring techniques. Stay at the forefront of MLOps and LLM Ops advancements, and identify and champion new tools and best practices. Design, develop, and implement robust MLOps and LLM Ops pipelines for efficient model training, deployment, and monitoring. Collaborate with cross-functional teams (engineering, data science, product) to ensure seamless integration of ML and LLM models into production workflows. Communicate complex technical concepts clearly to both technical and non-technical audiences. Mandatory Technical skills Must be abreast with CSP's (AWS, Azure and GCP) MLOPS and LLOPS stack. Should have expert level knowledge around deploying AI stack on Kubernetes cluster and scaling the same both horizontal and vertical. Must have experience on deploying AI applications using native services. Must have experience on VM provisioning and network set up Minimum over 10 years of experience in MLOps or LLM Ops along with a proven track record of successful deployments. In-depth knowledge of MLOps tools and platforms such as Kubernetes, Docker, Jenkins, Git, MLflow, LLM-specific tooling. Familiarity with Cloud platforms (AWS, Azure, Google Cloud) and infrastructure as code (IaC) principles. Experience in DevOps methodologies and CI/CD pipelines. Strong understanding of machine learning pipelines, model training frameworks, and monitoring techniques. Experience in large language models (LLMs) and their unique operational considerations is a plus. Good to have skills: - Excellent communication, collaboration, and problem-solving skills. Ability to translate technical concepts into clear and concise language. A passion for innovation and a drive to optimize ML and LLM workflows. Written and verbal skills especially to confidently express technical ideas and solutions.
