MLOps Engineer

Key Skills

Aartificial intelligence, AWS, Azure, Docker, Effective communication skills, Google Cloud Platform (GCP), Kubernetes-K8s, Machine learning techniques, MLOps tools, Python Programming

Job Description

As an MLOps engineer, you will be an integral part of our AI/ML team. You will help this team to streamline the process of getting models from proposed ideas to running in production environments.

Responsibilities

• 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