ML Engineer

ML Engineer

Requirements

• Experience Range: 7+ Years
• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• Proven experience in machine learning and statistical algorithms, with a strong portfolio of projects.
• Proficiency in machine learning frameworks, including PyTorch, TensorFlow, and Scikit-learn.
• Strong programming skills in Python and Pyspark, with a solid understanding of software engineering practices.
• Experience with data wrangling, transformation, and feature engineering for large datasets.
• Familiarity with version control systems (e.g., Git) and understanding of API and microservices architecture.
• Excellent problem-solving skills, attention to detail, and the ability to work collaboratively in a fast-paced environment.

Responsibilities

• Design, develop, and implement machine learning models using supervised and unsupervised learning techniques, deep learning, and reinforcement learning.
• Proficiently apply statistical algorithms and model evaluation techniques to ensure model accuracy and performance.
• Utilize frameworks such as PyTorch, TensorFlow, and Scikit-learn to build and optimize machine learning solutions.
• Perform large-scale data wrangling and transformation to prepare data for modeling and analysis.
• Collaborate with software engineering teams to integrate machine learning models into production systems, adhering to software design principles.
• Demonstrate programming proficiency in Python and Pyspark, ensuring clean and efficient code.
• Utilize version control systems such as Git to manage codebase and collaborate with team members.
• Understand and implement APIs and microservices to enable seamless integration of machine learning solutions.
• Stay updated with the latest advancements in machine learning and financial services, applying new knowledge to improve existing models and processes.

Preferred Qualifications

No preferred qualifications provided.