AI/ML Engineer

As a Machine Learning Engineer, your primary responsibility will be to design and implement end-to-end ML pipelines using GCP services and ETL tools. You will be deploying and maintaining predictive ML models through containerization (Docker) and orchestration (Kubernetes). It is essential for you to implement MLOps practices including CI/CD pipelines using Git. Additionally, you will 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 be crucial in optimizing big data processing workflows and improving ML model performance. To excel in this role, you must possess a strong background in Machine Learning and Deep Learning concepts. Extensive experience with GCP services, particularly AI/ML offerings, is a must. Proficiency in MLOps practices and tools is highly desirable. Hands-on experience with Docker, Kubernetes, and Git is required for this position. Your familiarity with frontend development and API integration will be beneficial. Knowledge of big data processing and analytics is essential. Furthermore, your ability to create clear technical documentation and architecture diagrams will be a significant asset to the team. Preferred qualifications for this role include Google Cloud certifications, experience with AI and GenAI models and applications, background in full-stack development, understanding of microservices architecture, experience with data pipeline optimization, as well as knowledge of Python, HTML, and Javascript. Join our team and contribute to cutting-edge projects that leverage machine learning technologies in a cloud-based environment.,