Promasidor was founded in 1979 by Robert Rose, who left the United Kingdom in 1957 for Zimbabwe to pursue his African dream. As Chairman of Allied Lyons Africa for over 20 years, he travelled extensively across Africa and gained a unique and thorough knowledge of the food industry throughout the continent. In particular he noticed a lack of availability of the one highly nutritious product that the developed world takes for granted - milk. He realised that with technology in the manufacture of milk powders advancing rapidly, there was an exciting opportunity to provide milk powder in small portions that could be packaged in flexible sachets. It was found that removing the animal fat from the milk and replacing it with vegetable fat allowed for a longer shelf life. This meant that for the first time, milk powder could be distributed across the vast African continent, providing access to affordable milk to everyone in Africa. A passionate belief in this vision fuelled the pioneering concept of selling filled milk powder in small sachets and Promasidor began selling the Cowbell brand in the Democratic Republic of Congo (then Zaire) in 1979. Today Cowbell is sold in the majority of countries across the African continent.
Introduction & Context
- Promasidor produces, distributes and markets a quality range of products in over 30 countries across the continent, reaching millions of consumers (more information available on our corporate web site here).
- Promasidor's IT Department (called PICT) is structured in 6 units with team members based across our main offices in Algeria, Angola, DRC, Nigeria, Ghana and South Africa:
- ERP: is responsible for everything related to our group ERP (Microsoft Dynamics Business Central) such as deployment, support & maintenance as well as functionality enhancements.
- GTM (Go-To-Market): is responsible for our Sales Force Automation (mobile) solution (internally built), used by over 2,000 users in 18 African countries.
- IS (Information Systems): is responsible for all business systems other than ERP and GTM (e.g.
- Business Process Management System for HR and other processes, Helpdesk ticketing solution, OEE for production efficiency, Quality Management System etc).
- BID (Business Intelligence Design): is responsible for the creation and maintenance of our reporting and analytics, mainly using Microsoft Power BI across all our business systems.
- OPS (Operations): is responsible for the infrastructure foundation for all the other units (physical and cloud, networking & connectivity, computers and other hardware devices) as well as productivity and collaboration tools (Emails, Microsoft Office & Teams, AI etc).
- Overview of the PICT organisation & reporting structure:
Job Purpose & Objectives
- Promasidor is accelerating responsible and practical AI adoption across its operations and seeks to create an AI unit comprising of AI Engineers and Analysts.
- The AI Engineer role is responsible for building, deploying, and maintaining AI solutions that integrate seamlessly with core Business Systems such as ERP (Business Central), GTM, IS, BI and internal applications.
- This role works closely with ERP, GTM, IS, BI, AI and Infrastructure teams to ensure AI capabilities are secure, scalable, reusable, and production ready.
- The role of AI Engineer can be performed: at four levels of seniority - Junior, Associate, Senior, and Lead - based on competencies, experience and performance. While each level has variations in the job description, all share the same overall characteristics described in this document.
Key Responsibilities & Activities
AI Engineering & Development
- Design and develop AI/ML models, LLM-based solutions, and RAG pipelines
- Build reusable AI services and APIs for integration into ERP, GTM, BI, and IS systems
- Deploy and manage AI workloads across cloud platforms (Azure, AWS, GCP as standardised)
- Implement prompt engineering, model tuning, and inference optimisation
Data & Platform Engineering
- Design and maintain data pipelines supporting AI use cases
- Work with BI, ERP, and GTM teams to integrate AI with enterprise data sources
- Ensure data security, access control, and compliance with AI usage policies
Collaboration & Integration
- Partner with ERP, GTM, IS, and BI engineers to embed AI into business workflows
- Support domain teams in consuming AI services correctly and safely
- Participate in architectural decisions to avoid duplicated AI stacks
Operations & Governance
- Monitor and optimise AI performance, cost, and reliability
- Maintain model lifecycle management (versioning, retraining, retirement)
- Enforce responsible AI, security, and privacy standards
- Implement MLOps practices to automate the deployment, monitoring, and retraining of models to ensure production stability
Testing & Fixing
- Perform unit testing, troubleshoot, and debug your own code during the development phase.
- Resolve software issues promptly during system tests and user acceptance testing (UAT) phases.
- Address software issues in production/live environments based on prioritized importance and urgency, aligned with your line manager.
- Technical Enablement & Knowledge Transfer
- Partner with the AI Analyst to create technical documentation that explains model logic, limitations, and data origins to build user trust in AI outputs.
- Provide technical support for domain teams (ERP, GTM, IS, BI) to ensure they can consume and integrate AI services correctly and safely.
- Ensure that AI interfaces (APIs or embedded UI elements) are designed with a focus on ease of use and production readiness to minimize technical barriers to adoption.
- Maintain the model lifecycle to ensure that as business needs evolve, the solutions remain relevant, accurate, and reliable for the end-user.
Personal Development
- Continuously stay updated and enhance your knowledge about the tools and technologies used within your unit.
- Stay informed about the evolution of emerging technologies and industry trends.
Key Competencies Requirements
Should Have
- Strong software engineering skills (Python preferred)
- Experience with Prompt Engineering
- Experience with cloud platforms (Azure, AWS, or GCP)
- Experience building APIs and integrating services
- Familiarity with ML/LLM concepts and data pipelines
- Experience with OpenAI, Azure ML, Gemini, or similar platforms
- Experience with RAG (Retrieval-Augmented Generation) architectures
- Experience with Vector Database Management (e.g., Pinecone, Weaviate) to support RAG pipelines
- Experience deploying AI models into production
- Knowledge of data security and governance principles
- Experience with FMCG, manufacturing, or supply chain systems
Job Requirements: Personal Attributes
- Attention to detail and commitment to quality.
- Strong work ethic and reliability.
- Adaptability and continuous learning.
- Initiative and proactivity.
- Resilience under pressure and ability to meet deadlines.
- Effective communication and teamwork.
Method of Application
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