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.
