AI Platform Lead

About INTERPOL

INTERPOL is the world’s largest international police organization, with 196 Member Countries. Created in 1923, it facilitates cross-border police co-operation, and supports and assists all organizations, authorities, and services whose mission is to prevent or combat international crime.

INTERPOL actively encourages applications from women and nationals of member countries that are currently unrepresented among our staff. Candidates from these countries are particularly encouraged to apply.

INTERPOL’s recruitment process is merit-based hence all hiring decisions are made considering the applicant’s qualifications and the needs of the Organization.

AI Platform Lead at INTERPOL

Within the Information and Communication Technology (ICT) Executive Directorate/Technology and Engineering, and reporting to the Head of Department Artificial Intelligence (AI) and Data Platform Competency Centre, the AI Platform Lead is responsible for the design, implementation, and operational management of INTERPOL’s central AI infrastructure.

The incumbent acts as the Technical Lead of the AI Cluster, managing the backend services (Large Language Models (LLMs), Retrieval-augmented generation (RAG) engines, workflow managers) that empower various digital products across the organization. The AI Platform Lead provides technical direction and mentorship to a team of Data Analysts and engineers, ensuring the delivery of a robust, scalable, and "AI-ready" platform without assuming direct HR management responsibilities.

Principal Duties and Activities

• Duty 1: AI Cluster & Platform Engineering
  • Own the architecture and lifecycle of the AI Cluster, ensuring high availability, scalability, and security of AI services in adherence to the principles set by the Engineering team.
  • Manage the deployment and serving of LLMs, Diffusion Models and Embedding Models (Open Source and Proprietary).
  • Build and maintain RAG pipelines and Vector Databases to ground AI responses in INTERPOL’s proprietary data.
  • Develop the "AI Backend" as a Service, exposing standard Application Programming Interfaces (APIs) for Product Squads (e.g., Digital Workplace for Police, Analytics...) to consume.
• Duty 2: Technical Leadership & Team Enablement
  • Act as the senior technical reference for the team of Machine Learning Operations (MLOps) Engineer, providing expert guidance on code quality, architectural patterns, and data engineering best practices.
  • Define and enforce LLMOps/MLOps standards for model development, testing, versioning, and deployment.
  • Conduct code reviews and technical workshops to upskill the team, fostering a culture of engineering excellence and automation.
• Duty 3: Workflow Orchestration & Agentic AI
  • Design and implement complex Workflow Managers (using frameworks like LangChain, AutoGen, N8N) to orchestrate multi-step AI tasks and agentic behaviors.
  • Ensure seamless secure integration between AI models, internal data sources, and external APIs.
• Duty 4: Operational Excellence & Optimization
  • Monitor the health, latency, and cost of AI workloads, proactively optimizing resource usage (Graphic Processing Unit (GPU)/ Central Processing Unit (CPU) allocation).
  • Continuously evaluate emerging AI technologies (e.g., quantization, new transformer architectures) to keep the platform at the cutting edge.
  • Collaborate with the AI Acceleration Lead to ensure the platform capabilities meet the evolving needs of business products.
  • Collaborate with the Engineering Team to ensure the principles are up to date with the cutting edge.
  • Forecast infrastructure evolution requirements in line with the Organization usage trends.
• Perform other related tasks as required by the hierarchy.

Qualifications, Competencies And Skills

Education and qualification required

• 3 to 4 years’ completed university degree (Master’s or equivalent) in Computer Science, Artificial Intelligence, Data Engineering, or a related field.
• Minimum of 5 years’ experience in software engineering or AI engineering, including hands-on implementation in production systems.
• Certifications in AI engineering, cloud services, data science, frontend development, or relevant emerging technologies are considered an asset.
• Proven experience in LLM integration, retrieval-augmented generation, vector-based search, or similar AI architectures.
• Proven experience in Platform Engineering and deploying AI/Machine Learning (ML) solutions in production environments.
• Demonstrated experience in technically leading or mentoring developers/data analysts.
• Experience in international or public-sector organizations is an asset.

Languages

• Fluency in English is required.
• Proficiency in other official language of the Organization (Arabic, French, Spanish) would be an asset.

Special Abilities required

• AI Platform Expertise: Deep understanding of LLM serving, RAG architecture, and Vector Search technologies.
• Engineering Stack: Proficiency in Python, API development (FastAPI), and container orchestration (Kubernetes/Docker).
• MLOps Mastery: Experience with model lifecycle management, monitoring, and Continuous Integration and Continuous Delivery (CI/CD) for AI.
• Workflow Orchestration: Experience with frameworks like LangChain, LlamaIndex, or temporal workflow engines.
• Ability to translate complex architectural concepts into actionable technical roadmaps.

Special aptitudes required

• Personal and professional maturity.
• Ability to maintain objectivity and apply logical, specifically inductive, reasoning.
• Ability to work in teams as well as individually.
• Ability to work persistently and under pressure.
• Good social, specifically multicultural skills.
• Initiative, creativity (original and critical thinking), and curiosity.
• Looking forward and attentive to innovation.
• Ability to develop and maintain good professional networks.
• Very good organization and listening skills.

Key skills/competency

• AI Platform Engineering
• LLM Deployment
• Retrieval-Augmented Generation (RAG)
• MLOps
• Kubernetes/Docker
• Python/FastAPI
• Technical Leadership
• Vector Databases
• Workflow Orchestration
• CI/CD for AI