AI Solutions Engineer

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

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. This includes knowledge graphs, ontologies, governance graphs, data lineage frameworks, and RAG pipeline architectures.

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.

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.