AI Trainer

Roles & Responsibilities

The ideal trainer should have strong hands-on experience with artificial intelligence and machine learning, with a focus on applying these technologies to solve real business and technical problems.

They must be able to clearly explain AI concepts, lead practical labs, and address ethical and operational aspects of AI.

Key Responsibilities:

• Conduct training sessions on AI and machine learning concepts for diverse audiences.
• Lead practical labs, hands-on exercises, and project-based learning.
• Clearly explain AI/ML concepts, model design, and evaluation techniques.
• Guide learners on ethical and operational considerations in AI adoption.
• Connect business and technical problems to AI/ML solutions.
• Advise on data collection, data preparation, and feature engineering processes.
• Support learners in model deployment, tuning, validation, and troubleshooting.

Requirements:

Technical Knowledge:

• Advanced understanding of AI/ML: supervised, unsupervised, deep learning, model evaluation.
• Skilled in data science: data prep, transformation, and handling multiple data types (text, audio, video, numeric, categorical).
• Proficient in programming (Python, R) and familiarity with ML frameworks.
• Knowledge of data collection, data transformation, and automation best practices.
• Solid grasp of AI ethics and responsible use.

Practical Experience:

• Experience building, deploying, and operationalizing ML models.
• Capable of supporting model deployment, security, maintenance, and ongoing monitoring.
• Able to instruct learners on handling business risk and ethical topics in AI projects.

Teaching Skills:

• Strong communication; able to break down complex AI topics for technical and non-technical learners.
• Experience in adult learning, training delivery, or leading interactive labs.
• Responsive to learner questions on model optimization, evaluation, and troubleshooting.

Domain Coverage:

• Understanding AI problems and applying AI to organization needs.
• Teaching engineering and feature selection for ML.
• Guiding on model training, tuning, risk, and evaluation.
• Leading on operationalization and real-world use of AI models.

Qualifications:

• Bachelor's or higher in Computer Science, Data Science, AI, Statistics, or related field.
• 1–3 years of professional experience with AI or data projects preferred.
• ACLP not required

Professional guidance and TTT will be provided.

Do note that this is a freelance position pegged to very competitive hourly rates (commensurates with experiences)