AI Engineering Mentor

peopleworth

. Facilitate online community discussions connecting weekly technical learning and build activities to real-world AI engineering practice.

Posted 10/9/2026contractRemote • United Kingdom, South AfricaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI engineering, particularly in building and operating LLM-based systems, while effectively facilitating learning and mentoring in online environments. Strong ability to communicate complex technical concepts and adapt guidance for diverse learner backgrounds.

Highest-signal resume keywords

LLM-Based Systems ExperienceAI Engineering ExpertiseProduction Engineering FundamentalsResponsible AI KnowledgeStrong Communication Skills

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

LLM APIsFunction CallingRAGRetrieval DesignAgent ArchitecturesEvaluation ApproachesCost OptimisationLatency OptimisationTestingCI/CD

Soft Skills

CommunicationFacilitationMentoringRelationship-BuildingJudgement

Tools & Technologies

ContainerisationMonitoringIncident ResponseOnline Learning Technologies

Industry Keywords

AI EngineeringSoftware EngineeringProduction EnvironmentsGovernanceAuditabilityBias AssessmentHuman OversightRegulatory Considerations

About the role

Key responsibilities & impact

Facilitate online community discussions connecting weekly technical learning and build activities to real-world AI engineering practice.
Design and deliver three 60-minute live industry sessions focused on practical application of learning topics.
Provide structured 30-minute one-to-one sessions supporting learners with industry context and career direction.
Share practical examples and lessons from building and operating LLM-based systems in production environments.
Support learners in understanding routes into AI engineering, employer expectations, portfolio credibility, and professional engineering practice.
Collaborate with delivery colleagues to flag learner concerns, share feedback, and contribute observations on programme content and learner engagement.
Help learners connect RAG, retrieval design, agent architectures, evaluation, monitoring, responsible AI, and production engineering to workplace scenarios.
Adapt guidance to learners with different starting points, including career changers, experienced coders, and practising software engineers.
Maintain boundaries around academic assessment by avoiding grading, direct correction of assessed work, or step-by-step guidance on assessed tasks.

Requirements

What you’ll need

Demonstrated professional software engineering experience with hands-on involvement in building and operating LLM-based systems in production.
Current professional experience in a role such as AI Engineer, AI Application Engineer, Senior or Staff Software Engineer, Machine Learning Engineer, Technical Lead, Principal Engineer, Architect, or AI Engineering Consultant.
Practical experience with LLM APIs, function calling, RAG, retrieval design, agent architectures, evaluation approaches, and cost or latency optimisation.
Strong production engineering fundamentals, including testing, CI/CD, containerisation, monitoring, and incident response.
Working knowledge of responsible AI in practical deployment environments, including governance, auditability, bias assessment, human oversight, and regulatory considerations.
Experience with AI-assisted software development, including an understanding of common failure modes and approaches for verifying AI-generated code.
Demonstrated ability to explain complex technical concepts clearly to people with different levels of technical experience.
Strong communication, facilitation, mentoring, and relationship-building skills, with the judgement to guide learners through uncertainty rather than provide overly prescriptive answers.
Comfort working with online learning and communication technologies.
Previous teaching, mentoring, facilitation, or online learning experience is desirable.
A master's degree is desirable but not essential.

Benefits

Comp & perks

Collaborative, people-centred performance culture.
Opportunities to grow in a fast-paced environment.
Opportunity to contribute current industry knowledge to the development of emerging AI engineering professionals.
Flexible participation in a remote learning environment, subject to the requirements of the role.