Machine learning Engineer

This role is for one of the Weekday's clients

Machine Learning Engineer

Support Vector Machines (SVM)

Requirements

Key Responsibilities

• Model Development & Deployment:
• Design, build, and deploy machine learning models tailored to real-world business problems.
• Implement and optimize Support Vector Machine (SVM) algorithms for classification, regression, and anomaly detection tasks.
• Ensure scalability and performance of deployed models in production environments.
• Data Management & Preprocessing:
• Work with structured and unstructured datasets to prepare clean, usable data for training.
• Apply feature engineering, dimensionality reduction, and data transformation techniques to improve model accuracy and efficiency.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and data insights.
• Algorithm Optimization:
• Experiment with various machine learning algorithms beyond SVM, including decision trees, ensemble methods, clustering, and neural networks.
• Fine-tune hyperparameters, optimize model performance, and validate results using rigorous statistical methods.
• Leverage techniques such as cross-validation, regularization, and kernel methods to enhance accuracy.
• Collaboration & Integration:
• Partner with data scientists, software engineers, and product teams to integrate machine learning solutions into business applications.
• Translate complex machine learning outputs into actionable insights for stakeholders.
• Support the creation of APIs and frameworks for easy deployment of ML models.
• Continuous Improvement:
• Stay updated with the latest advancements in machine learning, deep learning, and AI research.
• Explore novel approaches to enhance existing systems and processes.
• Contribute to building reusable ML components and maintaining best practices.

Required Skills & Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or related field.
• Experience: 3-5 years of professional experience in machine learning engineering.
• Core Expertise:
• Strong understanding and practical experience with Support Vector Machines (SVM).
• Solid knowledge of supervised and unsupervised learning techniques, classification, regression, and clustering.
• Programming & Tools:
• Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.).
• Experience with data visualization tools (Matplotlib, Seaborn, Plotly).
• Familiarity with version control systems (Git) and cloud platforms (AWS, Azure, or GCP).
• Analytical Skills: Strong background in statistics, linear algebra, probability, and optimization techniques.
• Soft Skills: Excellent problem-solving abilities, analytical thinking, communication, and teamwork.

Preferred Qualifications

• Experience with deep learning frameworks.
• Exposure to natural language processing (NLP) or computer vision projects.
• Familiarity with large-scale data processing frameworks like Spark or Hadoop