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.
MAIN PURPOSE OF JOBThe AI Machine Learning Research & Development 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, ML Flow, 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.
MAIN DUTIES
- The following is a non-exhaustive list of responsibilities and areas of ownership of an AI/ML R&D Engineer
- Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including machine translation, LLM finetuning, and quality assurance
- Take ownership of key projects from definition to deployment, ensuring that they meet technical requirements and maintain momentum and direction until delivery
- Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems
- Implement and optimize machine learning models and technologies using Python, TensorFlow, and other relevant tools and frameworks
- Perform statistical analysis and fine-tuning using test results
- Deploy machine learning models and algorithms using appropriate techniques and technologies, such as containerization using Docker and deployment to cloud infrastructure
- Use AWS technologies (including but not limited to Sagemaker, EC2, S3) to deploy and monitor production environments
- Keep abreast of developments in the field, with a dedication to learning in the role
- Document diligently and communicate thoughtfully about ML experimentation, design, and deployment
- Project scope: Define and design solutions to machine learning problems. Integration with larger systems done with the guidance of more senior engineers.
- Success Indicators for a Machine Learning 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 action.
- Ethical and Responsible AI Development: Adherence to ethical AI practices, ensuring models are fair, unbiased, and responsible.
REQUIREMENTS
- Education
- BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus
- Experience
- Minimum 3+ years experience as a Machine Learning Engineer or similar role
- Skills & Knowledge
- Ability to write robust, production-grade code in Python
- Excellent communication and documentation skills
- Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning
- Hands-on, high proficiency experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
- Experience with natural language processing (NLP) techniques and tools
- Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders
- Experience taking ownership of projects from conception to deployment, and mentoring more junior team members
- Hands-on experience with AWS technologies including EC2, S3, and other deployment strategies. Experience with SNS, Sagemaker a pls.
- Experience with ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc
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