AI Machine Learning Engineer
hace 2 meses
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
The Machine Learning R&D Engineer role is responsible for the design, development and implementation of machine learning solutions to serve our organization. This includes ownership or oversight of projects from conception to deployment with appropriate AWS services, Docker, MLFlow, and other. The role also includes responsibility for following best practices with which to optimize and measure the performance of our models and algorithms against business goals.Tasks and Responsibilities
- Machine learning model research and development: design, develop and deploy machine learning models for localization and business workflow processes, including machine translation and quality assurance. Utilize appropriate metrics to evaluate model performance and iterate accordingly.
- Ensure code quality: Write robust, well-documented, and structured Python code.
- Define and design solutions to machine learning problems: Work closely with cross-functional teams to understand business requirements and design solutions that meet those needs. Explain complex technical concepts clearly to non-technical stakeholders.
- Mentorship: Guide junior team members and contribute to a collaborative team environment.
Success indicators of a Machine Learning R&D Engineer:
- Effective Model Development: success is evident when the models developed are accurate, efficient, and align with project requirements.
- Positive Team Collaboration: demonstrated ability to collaborate effectively with various teams and stakeholders, contributing positively to project outcomes.
- Continuous Learning and Improvement: a commitment to continuous learning and applying new techniques to improve existing models and processes.
- Clear Communication: ability to articulate findings, challenges, and insights to a range of stakeholders, ensuring understanding and appropriate.
- Essentials
- Excellent, in depth understanding of machine learning concepts and methodologies, including supervised and unsupervised learning, deep learning, classification.
- Hands-on experience with natural language processing (NLP) techniques and tools.
- Ability to write robust, production-grade code in Python.
- Excellent communication and documentation skills. Able to explain complex technical concepts to non-technical stakeholders.
- Experience taking ownership of projects from conception to deployment. Ability to transform business needs to solutions.
- Nice to have
- Experience using Large Language Models in production.
- High proficiency with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Hands-on experience with AWS technologies including EC2, S3, and other deployment strategies. Experience with SNS, Sagemaker a plus.
- Experience with ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc.
- BS in Computer Science, Mathematics or similar field.
- Master’s Degree is a plus.
- 5+ years experience as a Machine Learning Engineer or similar role.
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