Machine Learning Engineer

Overview

Job Description - We are seeking a highly skilled Machine Learning Engineer to join our dynamic team at CodeArray Technologies Pvt Ltd. You will be responsible for designing, developing, and deploying machine learning models and AI solutions that address complex challenges. The ideal candidate is proficient in Python, PyTorch, and statistical modeling and has a strong passion for leveraging AI to create impactful tools.

Key Responsibilities

• Design, develop, and optimize machine learning models and algorithms.
• Implement and fine-tune deep learning models using frameworks like PyTorch and TensorFlow.
• Analyze large, complex datasets to extract insights and identify patterns.
• Develop scalable machine learning pipelines for training, validation, and deployment.
• Collaborate with cross-functional teams to integrate ML models into production systems.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and AI technologies.
• Document processes and model performance to ensure reproducibility and compliance.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 3+ years of experience in machine learning, deep learning, or AI tool development.
• Proficiency in Python and experience with frameworks such as PyTorch and Tensorflow.
• Solid understanding of statistical modeling, probability, and optimization techniques.
• Experience with data preprocessing, feature engineering, and handling large datasets.
• Strong problem-solving skills and attention to detail.
• Excellent communication and teamwork abilities.
• Ability to work in a fast-paced, dynamic environment and manage multiple priorities.

Job Type: Full-time

Pay: Up to ₹2,200,000.00 per year

Application Question(s):

• How many years of experience do you have ?
• Do you have experience on Pytorch & Tensorflow ?
• Do you have experience with Gen AI ?
• Location ?
• Current CTC ?
• Expected CTC ?
• Notice Period ?

Work Location: In person