Artificial Intelligence Engineer

Experis (Austria); Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Experis is seeking an AI Engineer to build semantic foundations, derived data assets, and knowledge graph capabilities for enterprise analytics. You will work at the intersection of data engineering and semantic modelling, transforming business concepts into reusable semantic models and trusted analytical assets.

You will collaborate with AI engineers, data architects, analytics engineers, and product teams to deliver a scalable platform with governed, explainable insights and robust

Qualifications

• Proven experience with semantic modelling and knowledge graphs.
• Strong SQL and data warehousing experience.
• Experience with Python and data processing.
• Experience with dbt or equivalent data transformation tools.
• Knowledge of governance, metadata, and ontology concepts.

Responsibilities

• Build semantic models, ontology artefacts, and graph projections.
• Develop derived assets such as aggregates and time-series outputs.
• Design scalable graph-loading pipelines and integrate with data platforms.
• Implement validation, data quality, and cost-efficient processes.
• Collaborate with AI engineers, data architects, and product teams.
• Contribute to governance, testing, and continuous platform improvement.

Skills

SQL

Python

Knowledge graphs

Ontology design

RAG concepts

Tools

dbt

Neo4j

BigQuery

Snowflake

Databricks

ClickHouse

Job description

What You'll Do

We're looking for an AI Engineer to help build the semantic foundations, derived data assets, and knowledge graph capabilities that power enterprise-scale analytics and AI solutions.

This role sits at the intersection of data engineering, analytics engineering, semantic modelling, and knowledge graphs. You will transform business concepts into reusable semantic models, develop trusted analytical assets, and enable graph-powered intelligence that supports decision-making at scale.

Working alongside AI Engineers, Data Architects, Analytics Engineers, and Product teams, you'll help create a robust platform capable of delivering accurate, governed, and explainable insights.

Implement and maintain semantic models, including entities, canonical fields, relationships, source mappings, grain definitions, and business transformations.

Develop deterministic derived assets such as aggregates, rankings, pivots, time-series outputs, and anomaly datasets.

Build validation processes covering data quality, warehouse-to-asset equivalence, deterministic SQL generation, materialisation, and cost efficiency.

Generate ontology artefacts and configuration-driven knowledge graph projections from semantic and derived asset metadata.

Design and implement scalable graph-loading pipelines using dimensions, reference data, and aggregated assets.

Integrate semantic and graph assets with enterprise warehouse platforms and analytical services.

Contribute across semantic compilation, ontology generation, graph projection, warehouse integration, and derived asset delivery based on programme priorities.

Support testing, optimisation, governance, and continuous improvement of semantic and knowledge graph platforms.

Experience designing and implementing semantic models, data models, or analytics layers.

Strong SQL development skills and experience working with modern cloud data warehouses.

Knowledge of data transformation and modelling tools such as dbt or equivalent frameworks.

Experience building or supporting analytical data products, reporting layers, or derived datasets.

Understanding of metadata, ontologies, taxonomies, and semantic technologies.

Experience with Python or similar programming languages.

Strong analytical and problem-solving capabilities.

Ability to work effectively within Agile delivery teams.

Knowledge graph experience using platforms such as Neo4j.

Understanding of ontology development and graph projection techniques.

Experience with ClickHouse, BigQuery, Snowflake, Databricks, or similar technologies.

Familiarity with AI, retrieval-augmented generation (RAG), and semantic search architectures.

Experience implementing data quality frameworks and automated validation.

Understanding of governance, lineage, and metadata management practices.