AI Tech Lead

The AI Tech Lead will drive the end-to-end design, architecture, and deployment of AI and machine learning systems to support the ICRC's mandate. This role involves translating operational challenges into production-grade intelligent systems while ensuring compliance with data protection, ethical AI, and operational resilience standards.

Responsibilities

• Design and architect scalable end-to-end AI/ML solutions aligned with business objectives and technical requirements.
• Lead the selection and implementation of tools, frameworks, and technologies for AI/ML model and system development.
• Develop machine learning models, algorithms, and AI solutions across applications such as natural language processing, computer vision, and large language models, covering data ingestion, feature engineering, model training, experimentation, versioning, and continuous delivery.
• Design AI infrastructure on Kubernetes clusters, including container orchestration and security hardening, and implement event-driven API integrations connecting AI services with existing ICRC platforms.
• Collaborate with product owners, data scientists, software engineers, and DevOps teams to integrate AI models into production systems, translating experimentation into scalable solutions.
• Establish AI/ML best practices across MLOps, model versioning, CI/CD pipelines, and production monitoring.
• Provide technical mentorship and guidance to engineering teams on AI/ML projects and initiatives.
• Monitor advancements in AI/ML technologies and apply relevant developments to address real-world business challenges.
• Ensure all AI/ML solutions comply with ICRC ethical standards, privacy regulations, and data security protocols.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
• 8+ years of experience in software engineering, with at least 4 years focused on AI/ML solutions architecture.
• Demonstrated track record of architecting and deploying machine learning or AI solutions at scale.
• Experience working in Agile delivery environments; SAFe certification preferred.
• Experience in a complex, multicultural international environment is an asset.
• Strong programming proficiency in Python and other relevant languages.
• Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras.
• Proficiency in designing and implementing machine learning models, including supervised, unsupervised, and reinforcement learning approaches.
• Solid understanding of MLOps practices, including model versioning, monitoring, and lifecycle management.
• Familiarity with deep learning architectures such as CNNs, RNNs, and Transformers.
• Good knowledge of AI concepts including RAG, Agentic AI, and MCPs.
• Expertise in containerisation and Kubernetes for scalable AI deployments.
• Proven experience in API design and integration across heterogeneous systems.
• Knowledge of GraphRAG or GraphDB is an asset.
• Knowledge of data privacy regulations and ethical considerations in AI.
• Fluent in English (written and spoken); French is a plus.

Skills

• Software Engineering
• AI Solutions Architecture
• Machine Learning Deployment
• Agile Development
• SAFe certification
• Python Programming
• TensorFlow
• PyTorch
• Scikit Learn
• Keras
• Machine Learning Model Design
• Supervised learning
• Unsupervised learning
• Reinforcement Learning
• MLOps principles
• Model versioning
• Model Monitoring
• Model Lifecycle Management
• Deep Learning Architectures
• CNNs
• RNNs
• Transformers
• AI Concepts
• Containerisation
• Kubernetes
• API Architecture
• API Integration
• Data Privacy Regulations
• Ethical AI Use

Languages

English, French