Job Description:
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.
