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 systems for various aspects of the localization (translation) and business workflow processes

Take ownership of key projects from definition to deployment, ensuring that they meet technical requirements and maintain momentum and direction until delivery

Experiment with and evaluate classical ML and LLM-based approaches (including prompt/context engineering, retrieval-augmented generation, and agentic workflows) to identify effective solutions for business problems

Perform statistical analysis based on experimental and test results to drive measurable performance improvements

Package and deploy machine learning systems using appropriate techniques and technologies

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.