AI Governance Graduate Officer (Remote) at Tongston Entrepreneurship Group

Company: Tongston Entrepreneurship Group

Location: Nigeria (Remote)

Employment Type: Contract

Job Category: IT/Telecom Jobs in Nigeria

Tongston is a multi-award-winning brand providing entrepreneurial education, media, enterprise & finance services for sustainable socio-economic development. T-World is Tongston’s AI powered digital entrepreneurial thinking ecosystem providing integrated media, enterprise, finance and entrepreneurial education services to K-12 Students, HE Students, Entrepreneurs, Intrapreneurs and their institutions globally to become Valuable, Influential & Profitable.

We are recruiting to fill the position below:

Job Title: AI Governance Graduate Officer

Overview

The role is responsible for shaping, governing, testing, and coordinating the responsible deployment of AI systems powering Tongston and T-World. The purpose of the role is to ensure that AI tools, assistants, agents, knowledge-base interactions, and AI-enabled workflows are clearly defined, ethically governed, properly tested, and aligned to real product needs across Tongston’s four pillars: Enterprise, Media, Finance, and Entrepreneurial Education.

The role owns the AI governance framework across the AI lifecycle, from use-case design and behaviour definition to prompt/KB interaction rules, knowledge-base admissibility, risk controls, output testing, governance registers, acceptance criteria, and release-readiness sign-off from a governance perspective. It ensures that AI systems are designed to improve user experience, support workflows, and uphold integrity, transparency, inclusiveness, safety, explainability, privacy, responsible data use.

The role does not own production backend or AI engineering implementation. Instead, it defines the AI behaviour, governance rules, testing expectations, source-use boundaries, risk controls, and acceptance criteria that AI Engineering and technology teams need in order to implement reliable AI-enabled features. It also coordinates with Education, Data, AI Engineering, Back End, Front End, UI/UX, Legal/Governance, and other relevant teams to ensure that AI systems are well-specified, responsibly managed, properly tested, and suitable for live or pilot product environments.

This role oversees AI governance testing, including hallucination review, bias review, safety review, KB admissibility review, source-use review, prompt/KB interaction review, weak-input testing, fallback testing, and escalation tracking. Where AI outputs are educational in nature, the role coordinates with Education AI Output Testers to ensure that governance-compliant outputs are also educationally accurate, curriculum-aligned, age-appropriate, pedagogically sound, and useful for users.

The role strengthens Tongston’s Entrepreneurial Thinking Model, reward systems, knowledge-base governance, and AI-enabled product experiences by ensuring that AI design, governance, testing, and implementation-readiness are connected, documented, auditable, and continuously improved.

Roles & Responsibilities

THINK (Conceptualisation & Design):

AI Domain Core Focus: AI Use Case, Behaviour & Governance Design

Own and document Tongston’s AI vision, governance model, and responsible AI operating approach across assistants, search, KB workflows, learning support, recommendations, content generation, analytics, and other AI-enabled features.

Define AI use cases across T-World widgets, including expected inputs, outputs, behaviour, user flows, boundaries, escalation points, and when AI should assist, defer, clarify, or not respond.

Translate responsible AI, data protection, privacy, transparency, explainability, inclusiveness, and safety principles into clear AI instructions, policies, rules, and implementation guidance for AI Engineering, Back End, Front End, UI/UX, Data, Education, and related teams.

Knowledge-Base Governance & Responsible Source Use

Own the governance approach for internal and external KB use, including allowed, restricted, and rejected content; metadata standards; source-use boundaries; attribution; licensing labels; permissions logic; and escalation rules.

Define and apply KB admissibility, source classification, and traffic-light frameworks in coordination with Licensors / Source Integrity Reviewers, Content Reviewers, KB Developers, Education, Legal/Governance, and AI Engineering.

Ensure downstream teams clearly understand what content may be used, how it must be labelled or attributed, when it must be escalated, and when it must not be used by AI.

Support KB organisation where needed, without replacing the dedicated KB Developer.

AI Risk & Ethics Frameworks

Lead identification, documentation, and management of AI risks, including hallucination, bias, unsafe responses, privacy risk, explainability gaps, source/KB misuse, overclaiming, low-confidence outputs, user confusion, and inappropriate reliance on AI.

Own key AI governance tools and registers, including hallucination, bias, safety/risk, KB admissibility, transparency, prompt/KB interaction, fallback failure, escalation, and remediation records.

Define governance acceptance criteria for AI-enabled features before release into live, pilot, or user-facing environments.

AI Testing Framework & Evaluation Planning

Own and maintain the AI governance testing framework, including test scenarios for hallucination, bias, unsafe responses, weak inputs, ambiguous prompts, restricted content, KB/source misuse, prompt/KB interaction failures, fallback failures, and low-confidence scenarios.

Define test cases, evaluation criteria, issue categories, scoring approaches, and re-test expectations for AI governance testing.

Ensure testing clearly distinguishes between governance testing, education-output testing, and technical engineering testing, while coordinating across the relevant teams.

Cross-Functional Integration Planning

Lead cross-team planning to ensure AI governance requirements, AI behaviour definitions, KB rules, testing expectations, and release-readiness dependencies are documented, tracked, and communicated.

Coordinate with Education, AI Engineering, Data/Research, and related teams where outputs require curriculum review, technical AI changes, metadata/retrieval updates, user-data inputs, analytics, research evidence, or reporting signals to support responsible AI design and evaluation.

CREATE (Development & Implementation):

AI Domain Core Focus: AI Flow Prototyping for Validation

Design or support simple AI flow prototypes using approved tools such as Flowise, Dify, or similar platforms to validate requirements, test governance rules, support walkthroughs, and clarify handover needs for AI Engineering.

Use prototypes to test expected AI behaviour, prompt structure, KB interaction, source-use rules, fallback logic, and user-facing output expectations.

Prototypes are for validation and governance support, not production implementation unless approved.

Knowledge-Base Development & Maintenance

Define prompt standards, prompt/KB interaction expectations, evaluation criteria, and test cases for AI-enabled workflows.

Run or oversee structured AI governance tests across approved prompts, dropdown inputs, free-text inputs, weak inputs, ambiguous queries, incomplete prompts, mixed intents, repetitive inputs, and other assigned AI interaction paths.

Review outputs for compliance with governance rules, including approved-source use, KB permissions, restricted-content controls, unsupported-claim avoidance, and appropriate behaviour in low-confidence or ambiguous scenarios.

AI Governance Testing & Issue Management

Maintain AI governance test logs, comparison outputs, screenshots/notes, issue summaries, re-test records, improvement notes, and related registers.

Identify, document, and track hallucinations, bias risks, unsafe responses, retrieval failures, KB gaps, source-use failures, prompt/input misalignment, fallback failures, weak responses, overclaiming, escalation decisions, and remediation status.

Knowledge Base Governance Operations

Define and apply KB governance rules, including metadata, licensing labels, admissibility rules, attribution requirements, source-use boundaries, and escalation logic.

Ensure source, permissions, and KB governance decisions are traceable, documented, organised, auditable, and usable by Licensors, Content Reviewers, KB Developers, Education reviewers, and AI Engineering.

Cross-Functional Implementation

Coordinate with Education, AI Engineering, Data/Research, Economics, Finance, and related teams to escalate education-quality issues, technical AI behaviour issues, and governed AI assumptions affecting outputs, analytics, VIP metrics, rewards, or digital-currency-related workflows.

Ensure implementation teams receive clear, testable, governance-ready requirements, while recognising that production AI engineering and backend implementation are owned by AI Engineering and the technology function.

SELL (Dissemination, Monetisation, Adoption & Impact):

AI Domain Core Focus: AI Transparency & Governance Reporting

Lead preparation of AI governance reporting outputs, including performance summaries, hallucination summaries, bias-risk notes, safety findings, source-use findings, KB admissibility summaries, fallback issue reports, and compliance-support documentation.

Prepare internal updates showing what changed, what risks were found, what testing revealed, what issues were escalated, what actions were taken, and what remains unresolved.

AI-Driven Innovation Showcases

Support demonstration materials and evidence packs showing how AI improves learning, productivity, content workflows, recommendations, matching, analytics, workflow support, and user experience.

Prepare or support walkthrough notes, screenshots, comparison tables, test results, prototype notes, issue summaries, and stakeholder-facing explanation materials, ensuring AI capabilities are presented responsibly and without overclaiming.

Monetisation & Adoption Strategy

Support the identification and packaging of AI-enabled offerings, such as AI assistants, analytics APIs, AI-supported workflows, custom KB experiences, premium AI features, or reporting products.

Contribute governance requirements, risk considerations, documentation, evidence, and adoption inputs in collaboration with Finance, Operations, Product, Data, Education, and AI Engineering.

Continuous Improvement & Feedback Loop

Analyse AI performance signals, governance testing results, user feedback, reviewer feedback, and recurring issue patterns to identify improvement areas.

Track weak outputs, hallucination patterns, source-use failures, prompt/KB interaction issues, fallback failures, user confusion, and usability concerns that should inform future AI design, governance, and engineering improvements.

Ensure continuous alignment between AI design, governance, testing, and real product behaviour so AI systems remain reliable, responsible, understandable, and effective in live or pilot environments.

Fulfil additional tasks as required by direct management in support of Tongston’s AI governance, product, knowledge-base, responsible AI.

Knowledge, Skills & Attitude Requirements

Knowledge:

Bachelor’s Degree, HND/ND, current study, or equivalent practical experience in Computer Science, IT, Information Systems, Engineering, Data Science, Statistics, Economics, Mathematics, or another relevant technology, analytics, or quantitative field is preferred. Relevant training, certifications, bootcamps, or practical learning in project coordination, IT support, data analytics, QA/testing, AI tools, digital operations, or governance-related work will be an added advantage.

Working understanding of AI systems in product environments, including LLMs, AI assistants, prompt design, prompt evaluation, basic retrieval/search, knowledge-base use, and how AI behaviour is defined, tested, and governed before implementation.

Understanding of responsible AI concepts, including bias, fairness, explainability, transparency, safety, privacy, consent, access control, data minimisation, retention, IP/licensing, and how these translate into logs, registers, evaluation records, and audit trails.

Understanding of knowledge-base governance for AI, including content sourcing, metadata tagging, attribution discipline, licensing labels, admissibility rules, basic retrieval concepts.

Exposure to AI/workflow tools such as Flowise, Dify, LangChain, LangGraph, LlamaIndex, ChatGPT, Claude, Gemini, open-source LLM interfaces, or similar tools for prototyping, testing, comparison, and evaluation.

Exposure to governance and KB tools such as metadata sheets, source registers, licensing registers, admissibility logs, bias/hallucination logs, safety/risk registers, AI issue trackers, test logs, evidence packs, and evaluation sheets.

Basic non-implementation awareness of API-based AI workflows, KB/retrieval systems, model routing, fallback logic, agent behaviour, and high-level Backend → AI → retrieval → database → frontend response flows.

Experience:

Up to 2 years’ relevant experience in an internship, NYSC, trainee, graduate, assistant, or junior role is preferred, particularly in AI design support, data or analytics, knowledge-base management, product or digital platform support, research, QA/testing, or technical documentation.

Experience supporting cross-functional teams and maintaining structured documentation (e.g., testing logs, issue trackers, audit trails, summaries, or compliance-related records) is an added advantage.

Technical Skills:

Ability to support AI prompt design, structured testing, output review, failure-pattern identification, and evaluation criteria development.

Ability to use approved AI/workflow tools such as Flowise, Dify, LangChain, ChatGPT-style tools, or similar platforms for prototyping, testing, documentation, and governance support.

Ability to support knowledge-base governance, including metadata tagging, content organisation, source tracking, licensing labels, attribution records, and Green/Yellow/Red classification.

Ability to maintain AI governance artefacts, including test logs, issue registers, bias/hallucination records, incident notes, content inventories, consent/attribution records, and performance summaries.

Ability to review AI performance at a basic level by identifying strong vs weak outputs, recurring edge cases, user-confusion risks, and issues requiring escalation or refinement.

Comfortable using spreadsheets, trackers, registers, and documentation tools to organise AI testing results, governance records, workflow notes, and stakeholder-ready summaries.

Attitude:

Strong ethics and integrity; careful handling of sensitive data and model outputs.

Detail-oriented, structured, and quality-focused (traceability, accuracy, repeatability).

Proactive improvement mindset: flags risks early, proposes fixes, and learns quickly.

Strong analytical, problem-solving, organizational and communication skills.

Ability to prioritize, and execute projects / tasks to a deadline with attention to detail.

You must align with and be determined to have a fit with Tongston’s culture.

Benefits

Monthly professional fee of NGN140,000.

A monthly Remote Productivity Support Package.

A company-provided laptop and access to Tongston-funded platforms, software, licensed tools and other approved resources required to perform your role effectively.

Pathway to full-time employment opportunity to secure additional benefits, including pension, HMO, statutory employee leave categories, promotion-based entitlements, and staff welfare benefits. See our Careers page for more info.

Exposure to user research, product strategy, user journeys, wireframing, prototyping, usability testing, analytics-informed design improvement, and cross-functional product delivery across web, mobile and other digital platforms.

Team-based execution and support, including an overall project management structure, reporting to a line manager, and collaboration within a sprint-based team workflow with defined weekly deliverables.

A structured learning and growth pathway supported through twice-yearly performance review, development and growth conversations (January & July). If you join after a review cycle, your first review will take place in the next review window. The review helps you document and communicate how you’ve grown, developed and demonstrated being Valuable, Influential & Profitable.

Learning & Development budget: NGN150,000 to support continuous upskilling, role-specific learning and broader professional development.

Up to 10 working days of approved paid time away from active delivery annually.

Eligible for the Tongston Annual Personnel High Performance Award which comes with cash prizes and certificates you can include in your profile for future jobs.

Professional Visibility: listed on Tongston’s website.

Complimentary premium access to T-World, Tongston’s integrated digital platform. Learn more at t-world.tongston.com

Eligibility for discretionary performance-based bonus/allocation

Participation in Tongston Academy sessions (scheduled periodically): Personal branding (includes CV/LinkedIn review), Personal finance & investing, Sales, communication & networking, Business development & strategy, Project management, Cybersecurity awareness & Digital automation tools.

Application Closing Date: 31st August, 2026.

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