AI/ML Ops Engineer

Role Overview: As a Machine Learning Operations Engineer, your primary responsibility will be to design and implement automated machine learning pipelines for data processing, model training, and deployment. You will collaborate with data scientists and ML engineers to ensure smooth integration of models into the production environment. Additionally, you will develop and maintain cloud infrastructure on Azure for machine learning models in production, monitor and optimize models for scalability and performance, and implement continuous integration and delivery pipelines. Key Responsibilities: - Design and implement automated machine learning pipelines for data processing, model training, and deployment - Develop and maintain cloud (Azure) 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 for model integration - Develop and maintain tools for monitoring, logging, and debugging machine learning models - Stay updated with the latest research and technology advancements in machine learning operations Qualifications Required: - 7+ years of relevant experience in designing and implementing ML Ops solutions on cloud platforms, preferably Azure - Proficiency in machine learning frameworks like TensorFlow or PyTorch - Strong expertise in programming languages such as Python or Java - Experience with deployment technologies like Kubernetes or Docker - Solid 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, tools, and best practices in MLOps - Strong analytical and problem-solving skills - Experience working with Agile methodology - Excellent communication and collaboration skills,