Senior AI/ML R&D Engineer

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Job Responsibilities:

The Senior AI/ML Engineer role is responsible for the discovery, 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 tools. The role also includes responsibility for establishing best practices with which to optimize and measure the performance of our models and algorithms against business goals.
The Senior AI/ML Engineer provides guidance and support to all members of the ML team.

MAIN TASKS & RESPONSIBILITIES

The following is a non-exhaustive list of responsibilities and areas of ownership of a Senior AI/ML 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 conception to deployment, ensuring that they meet business requirements and maintain momentum and direction until delivery
• Collaborate with developers, data engineers, and business process stakeholders to gather requirements and identify areas for improvement
• Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems
• Deploy machine learning models into a large-scale AWS environment, including architecture design and deep knowledge of AWS best practices
• Implement and optimize machine learning models and technologies using Python, TensorFlow, Pandas, and similar
• Perform statistical analysis and fine-tuning using test results
• Provide guidance and support to members of the team, mentoring in best practices for machine learning management
• 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: Design, scope, create, and deploy ML solutions into a large-scale cloud computing environment independently, without mentorship from more senior engineers.

Success Indicators for a Senior Machine Learning Engineer

• High-Quality Model Development: Success is reflected in the development of high-quality, efficient, and effective machine learning models that significantly contribute to solving complex business problems.
• Effective Team Leadership and Mentorship: Demonstrated ability in leading teams towards successful project completion and in mentoring colleagues for their professional development.
• Positive Cross-Departmental Collaboration: Effective collaboration with various departments, ensuring machine learning solutions are well-integrated and aligned with overall business objectives.
• Continuous Improvement and Learning: A commitment to continuous learning and improvement, both personally and within the team, keeping abreast of the latest developments in the field.
• Innovative Problem-Solving: A strong track record of innovative problem-solving, showing the ability to tackle complex challenges and optimize machine learning processes.
• Ethical and Responsible AI Development: A commitment to ethical AI practices, ensuring all developments are responsible, fair, and unbiased.

REQUIREMENTS

Education

• BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus

Experience

• Minimum 5+ 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
• Significant experience with ML management technologies and deployment techniques. Must include enterprise experience with AWS and Docker. Examples of relevant (but not required) AWS services: Amazon Sagemaker, EC2, Lambdas, DynamoDB, RedShift.

Additional Job Details: