Main responsibilities will involve:

Design and implement automated machine learning pipelines for data processing, model training, and deployment
Develop and maintain infrastructure for machine learning models in production
Monitor and optimize machine learning models for scalability, performance, and reliability
Implement and manage continuous integration and delivery pipelines for machine learning models
Collaborate with data scientists and ML engineers to ensure smooth integration of models to production environment
Develop and maintain tools for monitoring, logging, and debugging machine learning models
Stay up-to-date with the latest research and technology advancements in the field of machine learning operations.

Focus:

Designing, implementing, and maintaining automated machine learning pipelines, infrastructure, and deployment processes to optimize performance and reliability in the insurance industry. Skills and Experience

Strong understanding of machine learning algorithms, large language models and statistical modeling

Expertise in programming languages such as Python, Java, or C++
Experience in deployment technologies such as Kubernetes or Docker
Experience in cloud computing platforms such as Azure or AWS and cloud automation tools such as Terraform
Adept at learning and adapt to emerging trends, tools, and best practices in MLOps specific to large language models, such as new model compression techniques, pipeline automation, and monitoring solutions, to ensure efficient and scalable LLM implementations
Strong analytical and problem-solving skills
Experience in working with Agile methodology
Excellent communication and collaboration skills
Optional: Experience with machine learning frameworks such as TensorFlow or PyTorch

ML Ops Engineer