AI/ML Engineer

Role Overview: As a Machine Learning Engineer, your primary responsibility will involve designing and implementing end-to-end ML pipelines using GCP services and ETL tools. You will play a crucial role in deploying and maintaining predictive ML models through containerization (Docker) and orchestration (Kubernetes). It is imperative for you to implement MLOps practices, including CI/CD pipelines using Git. Your expertise will be instrumental in creating and maintaining technical architecture diagrams for GCP service implementations and developing frontend applications that integrate with ML and AI backend services. Furthermore, you will be tasked with optimizing big data processing workflows and enhancing ML model performance. Key Responsibilities: - Design and implement end-to-end ML pipelines using GCP services and ETL tools - Deploy and maintain predictive ML models through Docker and Kubernetes - Implement MLOps practices, including CI/CD pipelines using Git - Create and maintain technical architecture diagrams for GCP service implementations - Develop frontend applications that integrate with ML and AI backend services - Optimize big data processing workflows and improve ML model performance Qualifications Required: - Strong background in Machine Learning and Deep Learning concepts - Extensive experience with GCP services, particularly AI/ML offerings - Proficiency in MLOps practices and tools - Hands-on experience with Docker, Kubernetes, and Git - Familiarity with frontend development and API integration - Knowledge of big data processing and analytics - Ability to create clear technical documentation and architecture diagrams Role Overview: As a Machine Learning Engineer, your primary responsibility will involve designing and implementing end-to-end ML pipelines using GCP services and ETL tools. You will play a crucial role in deploying and maintaining predictive ML models through containerization (Docker) and orchestration (Kubernetes). It is imperative for you to implement MLOps practices, including CI/CD pipelines using Git. Your expertise will be instrumental in creating and maintaining technical architecture diagrams for GCP service implementations and developing frontend applications that integrate with ML and AI backend services. Furthermore, you will be tasked with optimizing big data processing workflows and enhancing ML model performance. Key Responsibilities: - Design and implement end-to-end ML pipelines using GCP services and ETL tools - Deploy and maintain predictive ML models through Docker and Kubernetes - Implement MLOps practices, including CI/CD pipelines using Git - Create and maintain technical architecture diagrams for GCP service implementations - Develop frontend applications that integrate with ML and AI backend services - Optimize big data processing workflows and improve ML model performance Qualifications Required: - Strong background in Machine Learning and Deep Learning concepts - Extensive experience with GCP services, particularly AI/ML offerings - Proficiency in MLOps practices and tools - Hands-on experience with Docker, Kubernetes, and Git - Familiarity with frontend development and API integration - Knowledge of big data processing and analytics - Ability to create clear technical documentation and architecture diagrams