AI/ML Engineer

As a Machine Learning Engineer, you will be responsible for designing and implementing end-to-end ML pipelines using GCP services and ETL tools. Your tasks will include deploying and maintaining predictive ML models through containerization (Docker) and orchestration (Kubernetes). It is crucial for you to implement MLOps practices, including CI/CD pipelines using Git. Additionally, you will be expected to create and maintain technical architecture diagrams for GCP service implementations and develop frontend applications that integrate with ML and AI backend services. Your expertise will play a vital role in 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,