Accountabilities:

AI Content Maintenance and Quality Assurance
Review, maintain, and improve existing AI and Machine Learning learning materials to ensure technical accuracy, relevance, and instructional quality.
Identify, investigate, and resolve urgent content issues, including broken code, failing technical labs, and solutions that no longer work with updated AI models, APIs, or libraries.
Review and revise educational content in accordance with established quality standards, technical requirements, and editorial guidelines.
Validate technical exercises, code examples, and learning environments to ensure they function correctly and provide the intended learning outcomes.
Maintain the quality and consistency of technical content across the learning catalogue.
Learner Experience and Continuous Improvement
Analyse learner and customer feedback, user testing results, and support data to identify recurring issues and prioritise content improvements.
Identify potential learner drop-off points and proactively revise educational materials to improve engagement, understanding, and completion rates.
Evaluate the effectiveness of existing learning resources and implement improvements that enhance the overall educational experience.
Collaborate with learner services, mentor operations, and solution architects to address technical and instructional challenges.
Tailor existing learning content into structured learning journeys aligned with individual learner needs and client objectives.
Content Development and Assessment
Review and approve content submissions from external or internal content contributors, ensuring they meet technical and instructional quality standards.
Validate proposed content improvements and confirm that accepted submissions deliver their intended learning outcomes.
Design and develop assessments to evaluate applicants' technical knowledge and readiness for specific learning programmes.
Apply instructional design principles to improve the clarity, structure, accessibility, and effectiveness of technical educational materials.
Contribute to the continuous evolution of AI-focused curricula, ensuring learning experiences reflect current technologies and industry practices.
Technical Collaboration and Problem Resolution
Work with cross-functional teams to investigate technical issues, implement content corrections, and coordinate improvements.
Use modern AI development tools and frameworks to validate technical solutions and maintain reliable learning environments.
Support the ongoing improvement of content maintenance processes, testing practices, and quality assurance workflows.
Report progress, identify recurring challenges, and communicate proposed improvements to relevant stakeholders.

Requirements

Professional Experience and Technical Expertise
At least 3 years of professional experience in Artificial Intelligence and Machine Learning, including hands-on experience with Generative AI and Large Language Models (LLMs).
At least 3 years of experience with Python and the Generative AI technology stack, particularly Hugging Face Transformers and PyTorch.
Working knowledge of scikit-learn, NumPy, and pandas for machine learning, data processing, and analytical tasks.
At least 1 year of experience developing LLM-powered applications, including Prompt Engineering, Retrieval-Augmented Generation (RAG), and evaluating AI-generated output quality.
At least 1 year of experience with Agentic AI, including tool calling, function calling, and single-agent workflows.
Familiarity with agent development frameworks such as LangGraph, CrewAI, OpenAI Agent SDKs, or Anthropic Agent SDKs.
At least 1 year of hands-on experience with managed cloud AI platforms such as Amazon Bedrock, Azure AI Foundry, Azure OpenAI, or Google Vertex AI.
At least 1 year of experience developing software using AI coding assistants or agents, such as Claude Code, Cursor, or GitHub Copilot.
At least 3 years of experience troubleshooting and repairing technical learning content, including debugging code, resolving technical issues, and fixing broken development or learning environments.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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