Machine Learning Engineer

As a Machine Learning Engineer, you will play a crucial role in designing, developing, and deploying machine learning models and systems. You will be responsible for working on projects that require knowledge of Milvus Vector Database, Retrieval-Augmented Generation (RAG) improvement, and various evaluation and accuracy assessment techniques. Key Responsibilities: Design, develop, and implement machine learning models and algorithms. Work with the Milvus Vector Database for efficient handling of vector data. Enhance Retrieval-Augmented Generation (RAG) systems for better performance and accuracy. Conduct evaluations of machine learning models and systems, ensuring they meet the required performance metrics. Collaborate with cross-functional teams to integrate machine learning solutions into products and services. Optimize and fine-tune models to improve accuracy and efficiency. Stay updated with the latest advancements in machine learning and AI technologies. Required Skills and Experience: 3-5 years of experience in machine learning or related fields. Strong knowledge and hands-on experience with the Milvus Vector Database. Proven experience in improving Retrieval-Augmented Generation (RAG) systems. Proficiency in evaluating machine learning models and systems. Solid understanding of accuracy metrics and techniques for improving model performance. Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience with data preprocessing, feature engineering, and model deployment. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills. Preferred Qualifications: Master s degree in Computer Science, Machine Learning, AI, or a related field. Experience with cloud platforms such as AWS, Google Cloud, or Azure. Familiarity with natural language processing (NLP) and related tools. Contributions to open-source ML projects or publications in ML conferences/journals. What We Offer: Competitive salary and benefits package. Flexible remote work environment. Opportunity to work with a talented and passionate team. Continuous learning and professional development opportunities. The chance to make a significant impact on cutting-edge projects.