AI Engineer (f/m/d)
About this role
Clariness is looking for an AI Engineer (f/m/d/) based in Germany.
Full-time; unlimited -
About the role:
This role is about turning AI and machine learning ideas into reliable, scalable solutions for Clariness' platforms. You will join a collaborative technical environment and work across the AI/ML lifecycle, from understanding a business problem and developing a solution to deployment, monitoring and continuous improvement within the regulated clinical-trial space.
As an AI Engineer, you would be responsible for:
Designing, building and operating production AI and machine learning solutions, including data pipelines, models and inference services.
Developing LLM-powered features using approaches such as retrieval-augmented generation (RAG), agent workflows, tool calling and structured outputs, with a focus on reliability, measurable quality and cost efficiency.
Delivering end-to-end machine learning workflows on Databricks, from data preparation and feature development through training and evaluation to batch and real-time deployment.
Maintaining quality in production through versioning, evaluation, monitoring and drift detection, and supporting the investigation and recovery of solutions when issues arise.
Collaborating with Product, Engineering, Commercial and Operations teams to translate business needs into scoped technical requirements, measurable success criteria and realistic delivery plans, including relevant data and privacy constraints.
Contributing to governance-ready data and AI practices covering quality, lineage, access, security, GDPR and responsible AI, including releva
Responsibilities
- Full-time; unlimited -
- Designing, building and operating production AI and machine learning solutions, including data pipelines, models and inference services.
- Developing LLM-powered features using approaches such as retrieval-augmented generation (RAG), agent workflows, tool calling and structured outputs, with a focus on reliability, measurable quality and cost efficiency.
- Delivering end-to-end machine learning workflows on Databricks, from data preparation and feature development through training and evaluation to batch and real-time deployment.
Requirements
- Maintaining quality in production through versioning, evaluation, monitoring and drift detection, and supporting the investigation and recovery of solutions when issues arise.
- Collaborating with Product, Engineering, Commercial and Operations teams to translate business needs into scoped technical requirements, measurable success criteria and realistic delivery plans, including relevant data and privacy constraints.
- Contributing to governance-ready data and AI practices covering quality, lineage, access, security, GDPR and responsible AI, including releva