We are hiring for a client of Moonhub.ai. We are dedicated to leveraging cutting-edge technology to solve real-world problems. We are seeking a talented Machine Learning Engineer to join our dynamic team and contribute to the development of innovative solutions.
Key Responsibilities:
Model Development: Design, develop, and deploy machine learning models that solve complex problems in various domains such as [e.g., natural language processing, computer vision, recommendation systems, etc.].
Data Preparation: Collect, clean, and preprocess large datasets for training and evaluating machine learning models. Ensure data quality and consistency throughout the process.
Algorithm Selection: Select appropriate machine learning algorithms based on the problem, dataset, and performance requirements.
Model Optimization: Optimize machine learning models for performance, scalability, and efficiency. Conduct hyperparameter tuning, cross-validation, and model validation.
Deployment: Integrate machine learning models into production environments, ensuring they operate efficiently at scale. Work closely with software engineers to deploy models in real-world applications.
Continuous Improvement: Monitor and maintain machine learning models post-deployment, iterating on them based on feedback, new data, and evolving requirements.
Collaboration: Work closely with data scientists, software engineers, and product teams to translate business requirements into technical solutions.
Research: Stay updated with the latest advancements in machine learning, artificial intelligence, and related fields. Apply new techniques and methodologies to improve model performance.
Documentation: Document the development process, models, and systems to ensure knowledge sharing and reproducibility.
Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field. A Ph.D. is a plus.
Experience: Proven experience as a Machine Learning Engineer or similar role, with hands-on experience in developing and deploying machine learning models.
Technical Skills:
Proficiency in programming languages such as Python, R, or Java.
Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
Strong understanding of algorithms, data structures, and mathematics (linear algebra, calculus, probability, statistics).
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes) is a plus.
Knowledge of big data tools and frameworks like Hadoop, Spark, or Kafka is advantageous.
Soft Skills:
Strong problem-solving skills and the ability to think critically and creatively.
Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders.
Team-oriented with a collaborative mindset.