AI & Data Engineer

Are you the AI & Data Engineer who wants to join our team to develop innovative data pipelines and state-of-the-art AI solutions?

As an AI & Data Engineer at iO, you will be working at the intersection of data, software engineering and AI technology. Within this broad range of activities, you will be responsible for the end-to-end development of software, platforms, tools and pipelines to get data from A to B and transform it into valuable insights.

You will tackle various AI problems : from the smart deployment or fine-tuning of Large Language Models, agent and multi-agent setups, and the application of RAG patterns, to building ETL pipelines that feed a data platform. The recurring pattern is that you integrate existing cloud services and APIs into an efficient, scalable and reliable whole.

Prompt engineering is an art form for you, where you think creatively about how to get the best results, with an eye for preventing hallucinations or applying smart guardrails. You evaluate your solutions in a systematic way to gain insight into the quality of the output.

In your work, you will encounter all kinds of AI: from statistical models to Large Language Models, from Generative AI in the field of images, video and audio to Natural Language Processing. It is mainly about deploying and applying existing models and services, and creating the infrastructure around them.

You will work on a wide range of customer issues: from automatically analysing documents to recognising events in audio or video data, building agents and orchestration solutions, generating graphical output based on data, or creating or validating content. Wherever AI can be applied, you, as an engineer, will implement the total solution.

You will work together with the various AI experts within iO and with various technical teams at customer sites. Your solutions will be based on modern (cloud) platforms, SaaS services and languages such as Type Script and Python.

• Devising AI and Data solutions:
  Analysing customer issues relating to AI and data and developing solutions, approaches and architecture.
• Data Pipeline Development:
  Designing and building robust ETL/ELT processes and real-time data streams.
• Data Platform Architecture:
  Setting up modern data warehouses, data lakes and analytics platforms.
• AI Integration:
  Implementing machine learning pipelines and integrating AI services into data workflows.
• Data Quality & Governance:
  Ensuring data quality, security and compliance within data ecosystems.
• Performance Optimisation:
  Optimising data processing performance and scalability.
• Cloud Data Services:
  Working with cloud-native data services such as AWS Glue, Azure Data Factory, or Google Data Flow.
• Monitoring & Observability:
  Implementing monitoring and alerting for data pipelines and AI workloads.