AI/ML Ops Engineer

As a Machine Learning Operations Engineer, you will be responsible for collaborating with data scientists and ML engineers to ensure a smooth integration of machine learning models into the production environment. Your role will involve developing and maintaining automated machine learning pipelines for data processing, model training, and deployment. You will also design and implement cloud infrastructure on Azure to support machine learning models in production. Monitoring and optimizing these models for scalability, performance, and reliability will be a crucial part of your responsibilities. Additionally, you will be in charge of implementing and managing continuous integration and delivery pipelines for machine learning models. Key Responsibilities: - Collaborate with data scientists and ML engineers to integrate models into the production environment - Develop and maintain tools for monitoring, logging, and debugging machine learning models - Stay updated with the latest research and technology advancements in the machine learning operations field Qualifications Required: - 7+ years of relevant experience in designing and implementing ML Ops solutions on cloud platforms, preferably Azure - Proficiency with machine learning frameworks like TensorFlow or PyTorch - Expertise in programming languages such as Python or Java - Experience in deployment technologies including Kubernetes or Docker - Strong understanding of machine learning algorithms, large language models, and statistical modeling - Familiarity with Azure cloud platform and Azure ML services - Ability to adapt to emerging trends and best practices in MLOps, particularly related to large language models - Strong analytical and problem-solving skills - Experience working with Agile methodology - Excellent communication and collaboration skills In this role, you will have the opportunity to contribute to cutting-edge machine learning projects, ensuring efficient and scalable implementations of large language models. Your expertise in ML Ops will be crucial in driving innovation and success in the field. As a Machine Learning Operations Engineer, you will be responsible for collaborating with data scientists and ML engineers to ensure a smooth integration of machine learning models into the production environment. Your role will involve developing and maintaining automated machine learning pipelines for data processing, model training, and deployment. You will also design and implement cloud infrastructure on Azure to support machine learning models in production. Monitoring and optimizing these models for scalability, performance, and reliability will be a crucial part of your responsibilities. Additionally, you will be in charge of implementing and managing continuous integration and delivery pipelines for machine learning models. Key Responsibilities: - Collaborate with data scientists and ML engineers to integrate models into the production environment - Develop and maintain tools for monitoring, logging, and debugging machine learning models - Stay updated with the latest research and technology advancements in the machine learning operations field Qualifications Required: - 7+ years of relevant experience in designing and implementing ML Ops solutions on cloud platforms, preferably Azure - Proficiency with machine learning frameworks like TensorFlow or PyTorch - Expertise in programming languages such as Python or Java - Experience in deployment technologies including Kubernetes or Docker - Strong understanding of machine learning algorithms, large language models, and statistical modeling - Familiarity with Azure cloud platform and Azure ML services - Ability to adapt to emerging trends and best practices in MLOps, particularly related to large language models - Strong analytical and problem-solving skills - Experience working with Agile methodology - Excellent communication and collaboration skills In this role, you will have the opportunity to contribute to cutting-edge machine learning projects, ensuring efficient and scalable implementations of large language models. Your expertise in ML Ops will be crucial in driving innovation and success in the field.