AI Engineering Industry Mentor

Peopleworth; Cape Town; A distancia; ZAR 600.000 - 1.000.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Collaborative culture

Career growth

Knowledge sharing

Remote learning

Descripción de la vacante

Peopleworth is seeking a practising AI engineering professional to bring current industry experience into a career-focused learning environment. The Industry Mentor role supports learners developing production-ready AI engineering capability, with emphasis on LLM-based application engineering and real‑world practice.

You will work alongside academic tutors, connecting industry standards to how AI systems are built, operated, governed and hired in industry today.

Formación

• 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

Responsabilidades

• Facilitate online community discussions that connect weekly technical learning and build activities to real‑world AI engineering practice
• Design and deliver three 60‑minute live industry sessions focused on the practical application of topics covered in the learning content
• Provide structured 30‑minute one‑to‑one sessions, through an agreed booking process, to support 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 relevant delivery colleagues to flag learner concerns, share learner feedback, and contribute observations on programme content and learner engagement
• Help learners connect technical topics including RAG, retrieval design, agent architectures, evaluation, monitoring, responsible AI, and production engineering to real workplace scenarios
• Adapt industry guidance to learners entering from different starting points, including career changers, experienced coders, and practising software engineers
• Maintain clear boundaries around academic assessment by avoiding grading, direct correction of assessed work, or step‑by‑step guidance on completing assessed tasks

Conocimientos

LLM systems

AI Engineer experience

Software engineering

Industry mentoring

Communication & mentoring

Online teaching/learning

Explain complex topics

Career guidance

Remote collaboration

Teaching experience desirable

Master's degree (desirable)

CI/CD & testing

Containerisation & monitoring

Educación

Master's degree

Herramientas

LLM APIs

Function calling

RAG

Retrieval design

Agent architectures

Evaluation approaches

Cost/latency optimisation

CI/CD tooling

Monitoring & incident response

Descripción del empleo

At peopleworth, we support work where people and performance thrive. As part of our Employer Group, we work with a variety of forward-thinking partners and are excited to share this opportunity that sits within our growing group.

Role Overview

This opportunity is for a practising AI engineering professional to bring current industry experience into a career-focused learning environment. The role supports learners developing production-ready AI engineering capability, with a particular focus on LLM-based application engineering, real-world engineering practice, career development, and industry context.

The Industry Mentor works alongside academic tutors while maintaining a distinct remit. Academic teaching, assessment, grading, and academic standards sit outside this role. The mentor focuses on connecting technical learning to how AI systems are built, operated, evaluated, governed, and hired for in industry today.

Requirements

• 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

Key Responsibilities

• Facilitate online community discussions that connect weekly technical learning and build activities to real‑world AI engineering practice
• Design and deliver three 60‑minute live industry sessions focused on the practical application of topics covered in the learning content
• Provide structured 30‑minute one‑to‑one sessions, through an agreed booking process, to support 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 relevant delivery colleagues to flag learner concerns, share learner feedback, and contribute observations on programme content and learner engagement
• Help learners connect technical topics including RAG, retrieval design, agent architectures, evaluation, monitoring, responsible AI, and production engineering to real workplace scenarios
• Adapt industry guidance to learners entering from different starting points, including career changers, experienced coders, and practising software engineers
• Maintain clear boundaries around academic assessment by avoiding grading, direct correction of assessed work, or step‑by‑step guidance on completing assessed tasks

Benefits

• 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

Throughout every stage, we value clear communication, respectful engagement, and timely feedback.