AI & ML Trainer

AI & ML Trainer Role

Location: South East Asia & The Gulf Area (Relocation required)

Salary: Competitive package, salary above market rates, accommodation and travel covered

Contract Type: Full-time

Company Overview

Our client delivers tailored cyber training and specialised products supported by modern technology and deep operational experience. Their programmes include realistic simulation environments, practical lab-based learning, and fully customised training paths that reflect the latest industry needs and emerging threats.

Their work supports government, defence, and commercial organisations, enabling teams to develop real-world capability in secure, controlled and immersive settings. They continue to invest heavily in research and development to ensure their solutions remain relevant, effective, and future-focused.

In this position, you will:

Join a team of instructors, responsible for our cybersecurity programs.

Conduct frontal teaching of information security and cyber courses.

Use Innovative technologies.

Develop and teach advanced labs.

Development of pedagogic materials and labs.

Package:

1) Competitive base salary (DOE)

2) Full coverage of travel expenses

3) Fully furnished accommodation with an office (relocation is required for this role)

4) No income tax (due to the nature of the location)

5) Full medical cover

Key Responsibilities

Training Delivery & Facilitation

• Deliver high-quality, hands-on training in AI, Machine Learning, and related domains (e.g. deep learning, NLP, data engineering)
• Facilitate practical labs, workshops, and scenario-based exercises using real-world datasets and problems
• Adapt delivery style to suit mixed audiences (engineers, analysts, operators, and leadership where required)

Curriculum Development

• Design and continuously enhance AI/ML training programmes (ranging from foundational to advanced levels)
• Develop structured learning materials including module descriptors, syllabi, presentations, labs, and assessments
• Ensure content reflects current industry practices, tools, and emerging trends

Practical Environment & Labs

• Design and build hands-on lab environments (e.g. Python-based ML pipelines, MLOps workflows, model deployment scenarios)
• Develop simulation-based exercises (e.g. anomaly detection, predictive maintenance, NLP pipelines, cyber/AI use cases)
• Integrate tools such as TensorFlow, PyTorch, Scikit-learn, and cloud platforms where appropriate

Mentorship & Capability Development

• Mentor students through complex technical topics and capstone projects
• Support learners in transitioning from theoretical understanding to applied capability
• Provide technical guidance and feedback to ensure competency development

Quality Assurance & Continuous Improvement

• Contribute to course reviews, assessment design, and quality assurance processes
• Ensure alignment with industry frameworks, standards, and best practices
• Continuously update content based on technological advancements and feedback

Stakeholder Engagement

• Work with internal and external stakeholders to align training with operational and business needs
• Support demonstrations, executive briefings, and capability showcase where required

Required Experience & Skills

Technical Expertise

• Strong hands-on experience in AI/ML development and deployment in real-world environments
• Proficiency in Python and common ML libraries (e.g. TensorFlow, PyTorch, Scikit-learn)
• Experience with data pipelines, feature engineering, and model evaluation
• Exposure to areas such as:
• Deep Learning
• Natural Language Processing (NLP)
• Predictive analytics
• MLOps and model lifecycle management
• Familiarity with cloud platforms (AWS, Azure, or GCP) is desirable

Practical / Industry Experience

• Demonstrated experience applying AI/ML solutions in operational or commercial environments
• Ability to translate business or operational problems into AI-driven solutions
• Experience working in environments such as cybersecurity, defence, finance, healthcare, or industrial systems is advantageous

Teaching & Communication

• Proven experience delivering technical training, workshops, or mentoring
• Ability to explain complex concepts clearly to both technical and non-technical audiences
• Strong facilitation skills with a focus on hands-on, applied learning

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field
• A PhD in a relevant discipline is advantageous but not essential
• Relevant industry certifications (e.g. cloud, data science, AI/ML) are a plus

Key Attributes

• Practitioner mindset with a focus on real-world application over theory alone
• Strong problem-solving and analytical thinking
• Passion for teaching and developing others
• Ability to operate in a fast-paced, evolving technical environment
• High level of professionalism and adaptability

Desirable Experience

• Experience in building or contributing to training academies or structured learning programmes
• Exposure to simulation-based training environments, cyber ranges, or applied AI labs
• Experience designing capstone projects or challenge-based learning (CTFs, scenarios, etc.)