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A pioneering role in one of the most rapidly evolving disciplines in applied AI, with genuine scope to define how it is practiced within a large organization. Deep technical immersion across the full AI stack, from data foundations and knowledge graphs to agentic systems and LLM orchestration. Close collaboration with both central and local data teams, as well as business stakeholders, giving you breadth and depth of exposure. The chance to build reusable AI assets and infrastructure that generate lasting business value at scale. A culture of experimentation, continuous learning, and knowledge sharing within a global, diverse team
Job Responsibility:
Design, implement, and continuously refine AI solutions and products, working across the full lifecycle from prototyping to production deployment
Work closely with central and local data teams to define, create, and maintain the organization’s context layer, optimized for AI use
Apply advanced prompt engineering, context engineering, memory engineering, and harness engineering techniques to maximize the performance and reliability of AI models in production
Translate business requirements and operational challenges into AI-transformed use cases and workflows, identifying where intelligent automation or augmentation can deliver the most value
Stay at the forefront of developments in the AI space, including the latest models, tools, and frameworks, and bring relevant innovations back into the team's practice
Contribute to the design of agentic AI systems and AI orchestration architectures, ensuring they are robust, scalable, and aligned with enterprise governance requirements
Document methodologies, prompt libraries, and context engineering standards to build reusable institutional knowledge
Requirements:
1-4 years of professional experience as a software engineer, data scientist, data engineer, or AI engineer
Strong programming skills in at least one modern language (Python is a plus, but not required), proficiency with Git, and solid software engineering practices
Hands-on experience building LLM-based systems, including prompt engineering and at least one orchestration framework (e.g., LangChain, LlamaIndex, LangGraph)
Solid understanding of Retrieval-Augmented Generation (RAG) pipelines and their design considerations
Deep understanding of general data science principles, including data quality, lineage, semantics, and governance
Strong awareness of the current AI landscape, including leading LLMs, multimodal models, agentic frameworks, and orchestration tools
Practical experience with knowledge graphs, ontologies, or semantic data models is a plus
Ability to combine technical rigor with practical business sense, turning real-world challenges into well-designed AI workflows
Strong communication skills, with the ability to explain complex AI concepts to non-specialist audiences
Fluency in English
additional languages are a plus
Experience in international, consulting, or scale-up environments is a plus
Nice to have:
Practical experience with knowledge graphs, ontologies, or semantic data models is a plus
additional languages are a plus
Experience in international, consulting, or scale-up environments is a plus