AI ML Research& Development Engineer

The following is a non-exhaustive list of responsibilities and areas of ownership of an AI/ML  Research & Development 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.

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