Machine learning engineer

2 days ago


Singapore Sociozk Full time

SocioZK is seeking a talented and motivated Machine Learning Engineer to join our innovative team. The ideal candidate will have a strong background in machine learning algorithms, data analysis, and software development. As a Machine Learning Engineer, you will be responsible for designing, building, and deploying machine learning models to solve complex business problems and enhance our AI-driven solutions.
Responsibilities
Model Development:
Design and implement machine learning models and algorithms for various applications, including predictive analytics, natural language processing, and image recognition.
Data Preparation:
Collect, clean, and preprocess large datasets from various sources to ensure high-quality input for machine learning models.
Feature Engineering:
Identify and create relevant features that improve model performance and accuracy.
Model Evaluation:
Conduct rigorous testing and validation of machine learning models, analyzing their performance using metrics such as precision, recall, and F1 score.
Deployment:
Collaborate with software engineers to deploy machine learning models into production environments, ensuring scalability and reliability.
Performance Monitoring:
Monitor model performance post-deployment, making necessary adjustments and retraining models as needed to maintain accuracy and effectiveness.
Collaboration:
Work closely with cross-functional teams, including data scientists, product managers, and software developers, to understand business needs and provide data-driven solutions.
Research and Innovation:
Stay updated on the latest advancements in machine learning and AI technologies, applying new techniques and methodologies to improve our products.
Required Qualifications
Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field; Master’s degree is a plus.
2-4 years of experience in machine learning, data science, or a related technical role.
Proficiency in programming languages such as Python, R, or Java, and experience with machine learning libraries (e.g., TensorFlow, Keras, Scikit-learn).
Strong understanding of machine learning algorithms, statistical methods, and data structures.
Experience with data manipulation and analysis tools (e.g., Pandas, NumPy, SQL).
Familiarity with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and MLOps practices.
Preferred Qualifications
Experience in deploying machine learning models using containerization tools (e.g., Docker) and orchestration platforms (e.g., Kubernetes).
Knowledge of deep learning frameworks and techniques.
Understanding of ethical considerations and best practices in AI and machine learning.
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