AI Platform Lead

About the ICRC

Since 1863, the International Committee of the Red Cross (ICRC) has worked to preserve human dignity and relieve suffering caused by war and armed violence.

In collaboration with our Red Cross and Red Crescent partners around the world, we do everything we can to deliver life-saving aid, reconnect families and locate missing people - helping those who need it most, regardless of who they are and what side of the front line they are on.

We engage with authorities and armed forces on all sides, often confidentially, pressing for access to detainees to improve their living conditions and urging compliance with international humanitarian law to protect non-combatants, including from digital threats.

Purpose of the Role

The ICRC is seeking an AI Platform Lead to own the hybrid AI platform that underpins the organization's centralized, transversal AI services (e.g. translation, entity recognition, specialized and generic LLMs). The platform covers AI services deployed on Kubernetes, model deployment on GPUs, the AI gateway and the self-service capabilities offered to advanced users.

The AI Platform Lead defines the platform vision and roadmap, leads its engineering and operation and manages its backlog. This enables product teams, data scientists and engineers to consume and build AI solutions securely and reliably in support of the ICRC’s mandate of protecting and assisting victims of armed conflict and other situations of violence. The role requires a good understanding of cloud-native infrastructure, GPU and LLM serving and open-source AI platform technologies.

Main Duties & Responsibilities

- Own the AI platform vision, roadmap and backlog. Prioritize features, improvements and technical debt with product owners, AI teams and other stakeholders, balancing user needs, security, cost and capacity.

- Manage the deployment and operation of centralized, transversal AI services (e.g. translation, entity recognition, specialized and generic LLMs) on the hybrid Kubernetes platform, making them reusable across ICRC products.

- Work closely with the Kubernetes product team on workload requirements, namespaces, resource quotas and release practices.

- Manage model deployment on GPU infrastructure. Serve open-source and third-party models (LLMs and other ML models) with appropriate inference engines, covering GPU scheduling and sharing, capacity planning, autoscaling, performance tuning and model release and rollback.

- Co-design and operate the AI gateway (e.g. LiteLLM or equivalent) with the security and network teams responsible for perimeter security and access controls. Run it as a single governed access point for model delivery (routing, fallback, access to internal and external models), Model Context Protocol (MCP) servers and tools, consumption monitoring (usage, quotas, cost tracking, logging) and guardrails.

- Design and operate self-service capabilities (e.g. OpenShift AI or comparable solutions) that enable advanced users to develop, train and deploy models in a governed environment, including workbenches, pipelines, model registry and resource quotas.

- Provide shared AI building blocks, such as vector and graph databases, agent components, MCP servers, model registry and experiment tracking, as documented services.

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- Establish platform engineering, MLOps and DevOps practices, including infrastructure as code, GitOps, CI/CD, monitoring and alerting, service-level objectives, incident and problem management, backup and disaster recovery.

- Ensure security, data protection and compliance by design. Work with information security, network and data protection colleagues on access control, secrets management, network segmentation and audit logging, so the platform supports ICRC ethical standards, privacy laws and data security protocols.

- Onboard AI workloads and users in collaboration with product owners, data scientists, software engineers, architects, the Kubernetes product team and infrastructure and security teams. Provide documentation, templates and support to platform users.

- Manage relationships with technology suppliers and contribute to capacity planning for GPU hardware, software licenses and support contracts.

- Provide technical leadership, mentoring and guidance to engineers and teams working on or with the platform. Stay current with developments in AI infrastructure and apply them to ICRC needs.

Education & Professional Background

- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related field, or equivalent professional experience.

- 10+ years of experience in software, DevOps or platform engineering, including 4+ years deploying and operating applications on Kubernetes-based platforms in production, and demonstrated experience with AI/ML or GPU workloads.

- Proven track record of deploying and operating production-grade AI or data services on Kubernetes (e.g. Helm, operators, resource quotas, ingress, persistent storage) in collaboration with a Kubernetes platform team.

- Hands-on experience deploying and serving ML and LLM models on GPU infrastructure, including GPU operators and drivers, GPU sharing and scheduling, inference engines and performance tuning.

- Experience designing and operating an AI/LLM gateway (e.g. LiteLLM), including model delivery, MCP, consumption monitoring and guardrails, working with security and network teams on perimeter and access controls.

- Experience in infrastructure as code, GitOps and CI/CD tooling (e.g. Terraform, Ansible, Helm, Argo CD, GitLab CI) and in integration with observability stacks (e.g. Prometheus, Grafana, OpenTelemetry).

- Proven experience managing a technical backlog and roadmap in an Agile delivery environment, translating user needs into platform capabilities.

- Certification or experience in Agile delivery. SAFe certification is an asset.

- Experience with self-service ML platforms (e.g. OpenShift AI, Kubeflow, MLflow) is a strong asset.

- Kubernetes certification (e.g. CKA, CKAD) is an asset.

- Experience in a complex, diverse international environment is an asset.

- Excellent written and spoken English. French is an asset.

Skills & Competencies

- Proficiency in Python and scripting languages.

- Good understanding of MLOps practices, including model versioning, registry, monitoring and lifecycle management.

- Good knowledge of AI concepts such as agentic AI and MCP, and of the services they require (e.g. vector databases). Knowledge of graph databases is an asset.

- Proven API design and integration skills across heterogeneous systems and partners.

- Knowledge of data privacy regulations, information security and ethical considerations in AI.

- Strong stakeholder management and communication skills, with the ability to engage technical and non-technical audiences, including senior leaders, and to manage suppliers.

Additional Information

- Location: Geneva, Switzerland

- Type of Contract: Open-ended

- Length of Assignment: Long term assignment

- Activity Rate: 100%

- Start date: January 2027

- Recruiter: Alejandra Rodriguez

- Application deadline: Thursday, 19 November 2026

Please note that the finalization of this recruitment is subject to the validation of the 2027 PfR.

The ICRC values diversity and is committed to creating an inclusive working environment. We welcome applications from all qualified candidates.

Are you ready to explore the next chapter of your career? Apply now!

For more information about working at ICRC HQ please click here .

Our Values

At the ICRC, we value impact, collaboration, respect, and compassion. We seek candidates who demonstrate behaviors based on these shared values. For more information on the ICRC values, please visit this page.