COMPANY 

AGL (Africa Global Logistics) is the leading multimodal logistics operator (port, logistics, maritime and rail) in Africa. The company is now part of the MSC Group, a leading shipping and logistics company. Thanks to its expertise developed over more than a century and more than 23,000 employees mobilized in 50 countries, AGL provides its African and global clients with comprehensive, tailor-made, and innovative logistics solutions, with the ambition to contribute sustainably to the transformations of Africa. AGL is also present in Haiti and Timor. 

Would you like to have a rewarding experience in an international environment? Make an impact in a company that places Africa at the heart of its project? 

Join Ascens Kigali, AGL's IT subsidiary, the leading multimodal logistics operator on the African continent! 

CONTEXT & MISSIONS

Based at the AGL Expertise Center, you will join the AI Factory within the Digital & Innovation Department as an AI Architect.

Your mission will be to define, design, and govern the overall AI architecture of the AI Factory, ensuring that all AI solutions are scalable, secure, interoperable, and aligned with AGL’s information system and industrial standards.

As part of the AI Factory, your responsibilities will include:

AI architecture design & governance

• • Define and own the AI Factory’s target architecture, including reference architectures, design principles, and technical standards applicable across all AI use cases

• • Design end-to-end AI solution architectures, covering data ingestion, model training, inference, serving layers, and monitoring components

• • Ensure consistency, scalability, and reuse across the AI portfolio by standardizing architecture patterns and avoiding fragmented implementations

• • Act as technical leader and submit architecture document to the Architecture and Security Board

Technology strategy & make-or-buy decisions

• Lead technology evaluation and selection processes, including foundation models, platforms, and tooling, based on performance, cost, and security constraints

• Define and maintain the AI Factory technology stack (LLMs, orchestration frameworks, vector databases, MLOps platforms, cloud services)

• Drive build vs. buy decisions and define model selection strategies across proprietary and open-source ecosystems

• Maintain a technology radar to anticipate and integrate relevant innovations into AGL’s AI architecture

GenAI, RAG & agentic architecture patterns

• Design and standardize RAG architectures (indexing strategies, embeddings, vector stores, retrieval optimization) for knowledge-intensive use cases

• Architect agent-based systems, including reasoning workflows, tool integration, memory management, and human-in-the-loop mechanisms

• Define prompt strategies, guardrails, and output validation to ensure reliability and consistency of GenAI applications

• Establish reusable patterns for multi-agent orchestration and complex AI workflows

System integration & enterprise alignment

• Design integration architectures connecting AI solutions to AGL’s core systems (ERP, TMS, data platforms, operational tools)

• Define API standards, event-driven architectures, and data contracts between AI services and enterprise applications

• Ensure alignment with enterprise architecture, cloud strategy, and IT standards in collaboration with infrastructure and architecture teams

• Guarantee end-to-end traceability, observability, and data lineage across AI pipelines

AI security, compliance & model protection

• Define and enforce AI-specific security standards, including model access control, data protection, and API security

• Design protection mechanisms against AI-specific risks (prompt injection, adversarial inputs, data leakage)

• Ensure compliance with regulatory and data sovereignty requirements across multiple countries and use cases

• Collaborate with security teams (CISO, IT Security) to integrate AI into AGL’s global cybersecurity framework

Technical leadership & AI Factory maturity

• Define and enforce engineering best practices across the AI Factory (testing, CI/CD, documentation, code quality)

• Conduct architecture and design reviews to ensure production-grade standards across all AI solutions

• Mentor AI Engineers on system design and production constraints

• Contribute to the AI Factory’s MLOps and LLMOps maturity roadmap

PROFILE 

Training & experience 

• Master’s degree or equivalent in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field.

• Minimum of 3 to 5 years of experience in software architecture, data engineering, or AI/ML engineering, including at least 3 years in an architecture or technical leadership role.

• Proven track record in designing and deploying large-scale, production-grade AI or data-driven systems within complex enterprise environments, with strong exposure to system integration and distributed architectures.

• Solid experience with modern AI paradigms, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based architectures, with the ability to translate these into industrialized architecture patterns.

• Strong background in cloud architecture, preferably Microsoft Azure, including the design and integration of AI workloads into enterprise or hybrid IT environments.

• Experience working with enterprise information systems (ERP, data platforms, APIs, event-driven architectures) and designing integration patterns between AI services and existing business systems.

• Exposure to governance, security, and regulatory constraints (data protection, auditability, AI compliance) applied to AI systems is strongly valued.

• Experience in multinational or multi-site environments, ideally within large-scale organisations or industrial contexts, with the ability to operate across diverse stakeholders and geographies.

Technical skills 

• Deep expertise in designing large-scale AI/ML architectures, including end-to-end pipelines from data ingestion to model serving, with a strong focus on scalability, reliability, and maintainability.

• Strong mastery of cloud architecture, preferably Microsoft Azure, including AI services (Azure AI Foundry, Azure OpenAI, Azure ML), API management, and deployment of AI workloads in enterprise environments.

• Advanced knowledge of LLM ecosystems and GenAI architectures, including RAG pipelines, agentic workflows, multi-model orchestration, prompt strategies, and system-level optimization trade-offs.

• Proven experience with AI orchestration frameworks and tooling and their integration into production systems.

• Expertise in system integration patterns, including API design, event-driven architectures, microservices, and integration with enterprise systems (ERP, TMS, data platforms).

• Solid understanding of MLOps and LLMOps concepts, including CI/CD pipelines, model lifecycle management, monitoring, observability, and retraining strategies

• Strong knowledge of AI security principles, including model protection, prompt injection defense, access control, data privacy, and secure API design within enterprise environments.

• Proficiency in software engineering fundamentals (Python, version control, testing, containerization) sufficient to guide teams and validate architectural decisions.

• Familiarity with AI governance frameworks and regulatory constraints (GDPR, AI Act), including auditability, traceability, and compliance-by-design in AI systems

Soft skills 

• Strong technical leadership, with the ability to define direction, make architecture decisions under uncertainty, and build alignment across multidisciplinary teams.

• Systems thinking mindset, with the ability to reason across the full stack — from data pipelines to user-facing applications — and anticipate the long-term impact of architectural decisions.

• Excellent communication and stakeholder management skills, with the ability to clearly explain complex technical concepts and trade-offs to both technical and non-technical audiences (including leadership and business teams).

• Pragmatic and delivery-oriented mindset, capable of balancing architectural rigor with speed of execution in a fast-moving AI environment.

• High level of intellectual curiosity and continuous learning, with a strong interest in emerging AI technologies and the ability to evaluate and translate innovations into practical architectural decisions.

• Strong collaboration skills, fostering knowledge sharing, mentoring engineering teams, and promoting a culture of technical excellence within the AI Factory.

• Strong sense of ownership and accountability, ensuring that all AI systems meet high standards of quality, security, and performance in production.

• Adaptability and resilience, with the ability to operate effectively in ambiguous, evolving environments and manage competing priorities across multiple AI initiatives

ADDITIONAL NOTES

Occasional collaboration with Digital Department, IT, business teams, support teams, AGL entities across several countries may be required and MSC Group AI Expertise

Comfortable working full time at the office (no remote)

Professional English and French are required.

📍 Position based in Kigali (Rwanda) at Norrsken House.