As an MLOps Engineer, you will be responsible for designing, implementing, and maintaining machine learning infrastructure, pipelines, and workflows.
Leverage your Python skills to develop and optimize code for machine learning workflows, data processing, and automation scripts.
Provide support for machine learning tasks, including data pre-processing, feature engineering, model training, and evaluation.
Utilize your expertise in Azure, GCP, or AWS to manage and optimize cloud infrastructure, ensuring efficient and scalable machine learning workflows.
Work closely with cross-functional teams including data scientists, software engineers, and DevOps specialists to align MLOps initiatives with business objectives and technical requirements.
Stay updated on the latest trends and advancements in MLOps, cloud computing, machine learning, and generative AI, and apply new knowledge to enhance our AI capabilities.
Qualifications
Bachelor s degree in CS or ECE.
Strong proficiency in Python.
4+ years experience in cloud engineering, with expertise in any of the cloud providers viz. AWS, Azure, or GCP
At least 1 year experience in MLOps, machine learning engineering, or a related role. Production level implementation and deployment for AI/ML use case.
Experience with Docker, Kubernetes (EKS/AKS/GKE)
Experience in MLOps tools such as MLFlow, Sagemaker, Kubeflow, etc.
Strong communication skills and ability to communicate analytical and technical content in an easily understandable way
