Full Stack MLOps Developer/Engineer

As a Full Stack MLOps Developer/Engineer, you will be responsible for designing, developing, and deploying scalable machine learning solutions and cloud-native applications, ensuring seamless integration and automation across platforms. Your role will involve collaborating with data scientists and DevOps teams to automate ML workflows and deployment processes. Additionally, you will be involved in developing and maintaining end-to-end full stack applications with a focus on ML deployment and automation. Key Responsibilities: - Develop and maintain end-to-end full stack applications with a focus on ML deployment and automation. - Design, implement, and manage scalable MLOps pipelines utilizing Kubernetes, Airflow, and Azure. - Collaborate with data scientists and DevOps teams to automate ML workflows and deployment processes. - Build robust backend services using Python and integrate them with frontend components developed in TypeScript. - Manage container orchestration and deployment strategies using Kubernetes. - Develop and enhance cloud infrastructure and services on Azure to support scalable ML solutions. - Implement monitoring, logging, and alerting for deployed ML models and applications. - Ensure code quality, security, and best practices are followed throughout the development lifecycle. Required Skills & Qualifications: - Proven experience in Full Stack Development with proficiency in Python and TypeScript. - Strong understanding of MLOps practices and tools. - Hands-on experience with Kubernetes (K8s) for container orchestration. - Experience with Apache Airflow for workflow automation. - Solid knowledge of Azure cloud platform services and infrastructure management. - Familiarity with Kadro (or equivalent workflow/automation tools). - Experience in deploying and managing ML models in production environments. - Strong problem-solving skills and ability to work in a collaborative team environment. - Excellent communication skills and ability to adapt to evolving project requirements. Preferred Qualifications: - Experience with CI/CD pipelines and DevOps practices. - Knowledge of data engineering and database management. - Prior experience working in a cloud-native environment.,