AI & Agent Development Engineer (Remote) at Tongston

Tongston, a multi-award-winning brand providing entrepreneurial education, media, enterprise and finance services for sustainable socio-economic development, is recruiting for the position of AI & Agent Development Engineer. 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.

Location: Remote Employment Type: Contract

Overview

Tongston is seeking an AI & Agent Development Engineer to translate Tongston's AI strategy, governance requirements, product specifications, KB logic, user-context logic, and model-routing decisions into production-ready AI workflows within a platform. This role is ideal for an early-career AI engineering professional who can help build, integrate, test, and improve AI-powered product features across the stack, including agent workflows, LLM-enabled experiences, retrieval logic, APIs, and supporting backend systems. This role is not limited to embedding third-party AI APIs. It requires practical understanding of LLM orchestration, open-source model selection, multi-model workflow design, retrieval systems, compute infrastructure, AWS-based deployment considerations, cost optimisation, and secure integration into T-World's existing GitHub codebase and platform architecture. It will operationalize the AI layer by combining approved open-source LLMs, selected closed-model APIs where justified, internal knowledge-base logic, user data, platform permissions, and governed workflow orchestration into reliable AI-enabled product features.

You will work closely with education, AI, data/research/economics team members; and a cross functional IT team of UX/UI designers, backend & front-end engineers, and other key team members/stakeholders. You will participate in Agile ceremonies including planning, development, testing, and iteration.

Core Stack:

Engineering & LLM Orchestration: Flowise, Dify, LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks; API-based LLM workflows; Open-source LLM integration and evaluation; Multi-model / multi-layer LLM orchestration; Dynamic function invocation using OpenAI / GPT-wrapper or equivalent where approved.

Back-End: Node.js with Express (API services / serverless routes where applicable); MongoDB (Atlas & Compass); API services and serverless routes where applicable.

Authentication: Firebase (synced with MongoDB).

Storage: AWS S3 including S3, Lambda, Step Functions, State Machines, Bedrock where applicable, and related cloud services.

CI/CD: GitHub Actions.

Front-End: Reactjs, Typescript, Tanstack-Router/Querry, Zustand.

Roles and Responsibilities

Build, implement, improve, and operationalize AI agents, assistant workflows, retrieval flows, and LLM-powered product features for T-World using approved tools, frameworks, orchestration platforms, APIs, open-source models, and code-based approaches as appropriate.

Translate product, user, AI governance, KB, and platform requirements into working technical flows, including user input handling, prompt orchestration, model routing, retrieval logic, guardrails, state handling, tool/function calling, fallback behaviour, and output delivery.

Support the development of T-World's proprietary multi-layer AI model by combining approved open-source LLMs, embeddings, retrieval systems, internal KB structures, user-context logic, and task-specific model routing into governed and reusable AI workflows.

Evaluate and recommend suitable LLMs or AI components for specific tasks, considering model capability, cost, latency, licensing, privacy, reliability, deployment feasibility, and whether the model should be used for free/basic functionality or premium/pay-per-use functionality.

Implement workflows that reduce unnecessary dependency on major closed-model providers, while still allowing approved premium model usage where advanced reasoning, accuracy, or performance justifies the cost.

Work with compute and cloud infrastructure considerations, particularly AWS, to ensure AI workflows can run, scale, store data, retrieve data, log activity, and handle latency or failure in a production-oriented environment.

Implement backend services, APIs, retrieval layers, storage logic, and integration logic required to power AI features across web and mobile product experiences, while aligning closely with the wider engineering architecture.

Incorporate AI-related code into the existing T-World GitHub codebase in a clean, reviewable, maintainable, and modular manner, using appropriate branches, pull requests, documentation, and handover notes.

Work closely with backend, frontend, and full-stack engineers to ensure clean AI integration across UI → API → AI orchestration → retrieval → database → output.

Implement application logic and structured data flows in MongoDB and related systems to support AI-enabled use cases, including retrieval, personalization, content mapping, user-specific recommendations, traceable workflow behaviour, and analytics-linked AI outputs.

Build, test, and optimize agent workflows using platforms such as Flowise, Dify, LangChain, LangGraph, LlamaIndex, or other approved orchestration tools, while supporting movement beyond no-code or low-code setups where greater control, customization, maintainability, or scale is required.

Implement retrieval and knowledge-base integration patterns, including storage and access logic for structured and semi-structured content, references, metadata, tagging logic, permissions, and linked evidence.

Implement technical logic that allows T-World AI to suggest, prefill, personalize, validate, or generate outputs across the platform, ensuring that outputs respect backend permissions, visibility rules, approved data-source rules, moderation states, and restricted-content controls.

Support AI-linked platform behaviour such as premium AI workflows, free-tier AI workflows, model fallback, usage limits, cost tracking, and escalation to higher-cost models only where justified.

Collaborate with other engineers on AI-enabled experiences in the app, including AI-powered assistants, search, recommendations, autofill, content generation, workflow suggestions, and widget-to-widget AI journeys.

Support latency management, loading states, fallback messages, retries, error handling, degraded-mode behaviour, and resilience patterns for AI-enabled features.

Handle weak input, no results, AI failure, timeouts, partial retrieval, partial model failure, and insufficient-context scenarios with clear fallback behaviour.

Log AI requests, model calls, retrieval events, outputs, failures, fallback events, premium-model usage, and relevant debugging information for review, monitoring, cost control, and future improvement.

Enforce backend permissions, visibility rules, moderation states, and KB approval status across all AI outputs.

Write clean, maintainable, secure, and well-documented code, ensuring clear separation across API, AI orchestration, model routing, retrieval, data, logging, and platform-integration layers.

Design and execute testing for AI systems, including weak or empty input, no-result scenarios, AI failure or timeout, model fallback, cost-trigger thresholds, consistency, permission enforcement, and workflow reliability.

Work with substantive reviewers, QA colleagues, AI Governance, Education, Data, Back End, Front End, and UI/UX teams to ensure that outputs are technically functional, contextually appropriate, governed, usable, operationally fit for purpose.

Support deployment, release readiness, monitoring, and iterative improvement of AI-enabled features in live or staging environments, including coordination on versioning, configuration, testing, observability, and stable release cycles.

Maintain technical documentation covering model choices, orchestration workflows, API dependencies, AWS/compute assumptions, retrieval logic, testing outcomes, implementation decisions, risks, and handover notes.

Escalate major technical, product, model, integration, compute, reliability, cost, privacy, or architectural risks to management in a timely manner.

Ensure all AI-enabled features are production-ready, reliable, cost-conscious, secure, maintainable, and aligned with Tongston's long-term goal of building T-World's AI layer.

Carry out additional tasks as required by management in support of product goals & req's.

Production-Readiness Expectations

All AI-enabled features built under this role must be suitable for real product environments, not just prototypes. This includes:

Clear handling of: AI failure or timeout; Weak or insufficient input; No-result retrieval scenarios; Partial system failure (e.g., retrieval works but AI fails).

Ensuring: Only approved and permitted data is used in outputs; Rejected or restricted content never appears in outputs; AI does not bypass backend validation or permissions.

Implementing: Fallback behaviour where AI confidence is low; Retry or degradation strategies where applicable; Structured logging for AI requests, failures, and outputs.

Designing for: Maintainability and handover; Traceability and debugging; Real-world latency and user experience constraints.

In addition, the role must ensure that AI-enabled features are designed for: Model-routing control; open-source and closed-model separation; cost optimisation and premium-use logic; compute and cloud infrastructure constraints; AWS deployment readiness where applicable; secure retrieval and storage; GitHub-based maintainability; traceability of model calls and outputs; clear fallback when cheaper/free models are insufficient; future extensibility toward a more proprietary T-World AI layer.

The role is expected to think beyond “the AI works” and ensure that the AI system is reliable, safe, scalable, cost-conscious, maintainable, and strategically owned by Tongston as much as possible.

Knowledge, Skills & Attitude Requirements

Experience Level: This role is most ideal for early-career engineers who has hands-on experience building backend systems and has also begun working practically with LLMs, AI agents, retrieval workflows, or AI-enabled product features. Relevant experience may include personal projects, internships, volunteering, open-source work, hackathons, freelance engagements, research projects, or professional roles involving AI workflows, backend engineering, agent integration, API integration, cloud infrastructure, or product implementation. The ideal candidate does not need to have trained a foundation model from scratch, but must understand how modern AI systems are practically assembled using APIs, open-source models, orchestration frameworks, retrieval systems, user-context logic, cloud infrastructure, and product code.

Technical Knowledge & Skills

Degree, HND/ND, current study, or equivalent practical experience in Computer Science, Software Engineering, Data/Analytics, Information Systems, Mathematics, Statistics, Engineering, AI/ML, or related fields.

Working knowledge of Node.js, Express, REST APIs, third-party API integration, internal service integration, and server-side logic required to connect user actions, platform data, AI workflows, storage, authentication, and output delivery.

Working knowledge of MongoDB, MongoDB Atlas, and MongoDB Compass, including schemas, collections, query logic, data validation, debugging, and structuring/retrieving data for AI-enabled use cases.

Experience or strong practical exposure to LLM APIs, agent workflows, prompt orchestration, retrieval logic, tool/function calling, model routing, fallback logic, and structured AI integrations for product features such as suggestions, autofill, personalisation, recommendations, search, and content generation.

Working awareness of open-source/lightweight LLMs, small language models, domain-specific models, embedding models, rerankers, and model repositories such as Hugging Face; with ability to assess models based on task fit, accuracy, latency, cost, licensing, privacy, deployment feasibility, and integration constraints.

Ability to support Tongston's goal of building a proprietary T-World AI layer by combining approved models, retrieval systems, internal KB logic, user-context logic, permissions, and governed orchestration flows, rather than relying only on major closed LLM providers.

Familiarity with AWS and AI-related infrastructure, including S3, Lambda, Step Functions, State Machines, Bedrock where applicable, storage, logging, scaling, latency, cost, and the compute infrastructure needed to run, call, host, or orchestrate AI/LLM workflows.

Familiarity with authentication, Firebase Authentication, secure access patterns, backend visibility rules, moderation states, approved data-source rules, and how to ensure AI outputs do not bypass platform permissions or backend validation.

Ability to contribute AI-related code into the existing T-World GitHub codebase using branches, pull requests, review cycles, documentation, issue tracking, and release workflows, while avoiding disconnected prototypes.

Ability to test and troubleshoot AI workflows across UI → API → AI orchestration → retrieval → database → output flows, including weak input, no-result retrieval, timeout, failure, fallback behaviour, logging, monitoring, traceability, maintainable code, and handover documentation.

Soft Skills & Attitude:

Strong analytical and problem-solving skills; Clear written and verbal communication; Ability to prioritize and execute tasks independently in a remote environment to schedule; High attention to detail and ownership mindset; Curious, proactive, and committed to continuous learning; Strong alignment with Tongston's mission, values, and culture. Learn more about Tongston's culture: [Click the Apply button below to apply, and Create my CV to build a CV tailored to this offer, professionally] Knowledge & Skills

Experience building internal tools, admin panels, or workflow automation that support KB maintenance, QA, review, analytics, reporting, or operational oversight.

Ability to collaborate with front-end engineers on AI-linked UI behaviour, including prompts, suggestion panels, autofill options, loading states, fallback messages, error states, review/edit flows, and generated-output displays.

Experience with LangChain, LangGraph, LlamaIndex, Flowise, Dify, or similar orchestration frameworks.

Experience with vector databases, embeddings, semantic search workflows, reranking, chunking, metadata filtering, and retrieval evaluation.

Experience with AI-powered search, assistants, recommendations, content generation, autofill, summarisation, classification, or related AI-enabled product features.

Exposure to CI/CD, GitHub Actions, deployment pipelines, staging environments, and production support practices.

Experience with model evaluation, prompt testing, AI workflow testing, red-teaming, fallback testing, or quality assurance for AI outputs.

Basic Web3 exposure where relevant to wallets, signing flows, token interactions/conversion, and on-chain vs off-chain boundaries.

Salary and Benefits

Monthly professional fee of NGN175,000. 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: NGN200,000 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. 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.

Method of Application

Submit your CV and Application on Company Website: Click Here. Important Information and Notice: Where you can provide information about yourself, upload your CV & portfolio where applicable, and answer some basic pre-screening questions that demonstrate how you meet the knowledge, skills and attitude requirements for the role, in line with the JD. Applications will be reviewed on a rolling basis, so we strongly encourage early submission.

If you have any questions or clarifications, please write to us at: [email protected] with [email protected] in copy. Please use the subject “IC Application for - [name of the role]” in directing any correspondences to us.

If shortlisted, you will progress through the rest of the recruitment process - technical assessment, personality & multiple intelligence check, conversation with the line manager and one or more team members, reference checks, and, if successful, an offer & if accepted, onboarding.

Although this is a remote opportunity, candidates based in Gombe, Bauchi, Ilorin, Lagos or Abuja will be viewed favourably. For candidates who have not yet completed their NYSC, we may also be able to consider an NYSC engagement, particularly for those based in Gombe, Lagos or Abuja.