Job Title

The AI Systems Mentor at PRENXIS is a hybrid role that combines builder, operator, and mentor responsibilities into a single execution-focused function. The core mandate is not to "teach AI," but to lead students through the end-to-end creation of real AI systems—from problem identification and prompt design to coding, validation, deployment, and monetisation. This role sits at the intersection of technical depth (Python, LLMs, automation) and practical application (business use cases, client-ready systems), ensuring that every learning interaction results in a tangible, deployable output rather than passive understanding. Success in this position is defined by student output and measurable outcomes, not session delivery. A high-performing mentor consistently enables students to build production-grade systems such as RAG-based assistants, automation workflows, and revenue-generating AI tools. Key indicators include: students maintaining active GitHub portfolios, deploying live applications, successfully debugging and iterating on systems, and ultimately generating income or securing roles using their work. The mentor's effectiveness is visible through the quality, quantity, and usability of student projects, as well as the speed at which students transition from learners to independent builders. Within the organization, this role is mission-critical and central to PRENXIS's differentiation strategy. While marketing attracts students and curriculum defines the structure, the mentor is the execution engine that converts intent into results. They directly influence product quality, brand reputation, student success rates, and downstream revenue (placements, freelance outcomes, referrals). Functionally, the role operates as a bridge between curriculum design, student experience, and real-world industry application, ensuring alignment between what is taught and what the market demands. In strategic terms, this position transforms PRENXIS from a conventional academy into a high-output AI production ecosystem, where learning is inseparable from building and earning. Responsibilities of Mentor: - Lead live, execution-first sessions where learners build real AI systems (no slide-based teaching); ensure every session results in a working output. - Guide learners through the end-to-end AI development lifecycle: problem definition → prompt design → coding → validation → deployment → monetisation. - Mentor learners in building production-grade systems, including: RAG-based applications, automation workflows (APIs, n8n), multi-agent architectures, sales, retention, and support AI tools - Provide real-time debugging support across Python, APIs, LLM outputs, and system integrations; demonstrate structured problem-solving approaches. - Enforce Proof-of-Work standards: weekly GitHub commits, Clean, documented repositories, functional, deployable projects - Review and improve learner projects with actionable technical feedback (code quality, architecture, performance, reliability). - Support learners in deploying applications using tools such as Docker, VPS, and Streamlit; ensure systems are live and usable. - Implement and teach AI reliability practices, including: hallucination detection, prompt validation, edge-case testing, monitoring and logging - Help learners translate technical work into business value, including: identifying use cases, estimating ROI, packaging solutions for clients - Coach learners on freelancing and income generation, including: service packaging, proposal creation, pricing strategies, client communication - Track learner progress and ensure consistent output velocity (daily practice, weekly builds, monthly deployments). - Collaborate with internal teams to refine curriculum and projects based on industry trends and learner performance. - Maintain personal involvement in building and experimenting with AI systems to keep sessions current, practical, and industry-relevant. - Foster an inclusive, supportive environment where learners of all backgrounds can build confidence, ask questions, and progress through hands-on execution. Qualifications Core Skills (Essential) - Strong proficiency in Python (automation scripts, APIs, data handling, error handling) - Hands-on experience with LLM-based systems: - RAG pipelines (retrieval + generation) - Frameworks such as LangChain, LlamaIndex, or similar - Experience building automation workflows (e.g., n8n or API-based systems) - Ability to design and implement end-to-end AI systems (from idea to deployment) - Working knowledge of deployment tools: - Docker, VPS/cloud environments - Streamlit or similar app frameworks - Strong debugging and problem-solving skills across code, APIs, and AI outputs Practical Experience (Mandatory) - Demonstrated experience building real-world AI or automation projects - Active GitHub portfolio with clean, documented repositories - Experience deploying at least 2–3 live applications or systems - Exposure to business use cases (sales automation, customer support, operations, etc.) Teaching / Mentoring Capability - Ability to explain complex technical concepts in a clear, structured, and practical manner - Comfortable conducting live coding / build sessions - Experience mentoring, guiding, or collaborating with others (formal or informal) Preferred Experience (High Advantage) - Freelancing or client-based project experience (e.g., delivering AI/automation solutions) - Experience working in startups, SaaS, or product environments - Familiarity with multi-agent systems or advanced AI workflows - Experience with monitoring, logging, and AI reliability practices Education - Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (preferred but not mandatory) - Equivalent practical experience is highly valued over formal degrees Certifications (Optional) - Certifications in Python, AI/ML, or cloud platforms (not required) - Demonstrated proof-of-work projects are preferred over certifications