AI Machine Learning R&D Engineer

MAIN PURPOSE OF JOB

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
• Master degree in Computer Science, Data Science, Engineering, Mathematics or similar field; PhD 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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