Machine Learning Engineer

Company Overview

Company details are not specified.

Job Summary

The Machine Learning Engineer will be responsible for developing and implementing machine learning models utilizing both supervised and unsupervised learning techniques. This role aims to enhance the organization’s capabilities through advanced data analysis and artificial intelligence technologies, including generative AI, retrieval-augmented generation (RAG), and AI agents.

Responsibilities

• Design and develop machine learning models for various applications using both supervised and unsupervised learning techniques.
• Implement generative AI solutions, focusing on RAG and AI agents.
• Collaborate with cross-functional teams to analyze business problems and define model objectives.
• Optimize and scale machine learning algorithms for effective performance in production environments.
• Evaluate model performance and conduct experiments to enhance accuracy and efficiency.
• Document processes and findings to facilitate knowledge sharing across the team.

Qualifications

• Proficiency in Python programming language for developing machine learning models.
• Strong understanding of machine learning concepts, including supervised and unsupervised learning methodologies.
• Experience with generative AI, specifically with RAG and AI agents.
• Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, or a related discipline) is preferred.
• Familiarity with data preprocessing techniques and model evaluation metrics.
• Excellent analytical, problem-solving, and communication skills.

Preferred Skills

• Experience with additional programming languages (e.g., Java, R) is a plus.
• Knowledge of specific ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying machine learning solutions.

Experience

• Relevant experience in machine learning model development is preferred, although specific years of experience are not specified.

Environment

Work environment details are not specified, such as remote, in-office, or hybrid settings.

Salary

Salary details are not specified.

Growth Opportunities

Potential career advancement opportunities are not specified.

Benefits

Information regarding benefits is not provided.