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# Senior AI Solution Engineer

- **Company:** Eversports
- **Location:** Wien, Austria
- **Experience:** Expert
- **Salary:** €72,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Applications Architecture, Software as a Service, Large Language Models, Snowflake, Prompt Engineering, Software Coding
- **Published:** September 12, 2026
- **Apply:** https://www.adzuna.at/details/5879862572

## About the Role

* 5+ years of engineering experience, including at least 18 months building production LLM/AI application systems.
 * A track record of end-to-end ownership of AI applications in production: discovery, design, build, evaluation, and operation, not just one stage of the pipeline.
 * Experience running measurable, quantitative evaluation of AI in production: defined evaluation criteria, experiments (A/B or equivalent), and iteration driven by production metrics and real user behaviour.
 * Production experience with RAG, prompt engineering, and LLM application evaluation.
 * Strong product sensibility at the application level, comfortable moving between "what should this do for the user" and "how do I build it."
 * A track record of working directly with non-technical stakeholders on technology adoption.
 * Solid production cloud experience is required. AWS with Bedrock is strongly preferred, as the group runs on AWS/Bedrock.
 * Hands-on coding fluency.
 * Strong written and verbal communication skills.
 * On-call experience for production systems., * Experience in SaaS, marketplaces, or two-sided consumer platforms.
 * Experience with AWS, Snowflake, and dbt.
 * Familiarity with evaluation tooling for LLM applications.
 * Knowledge of GDPR and EU AI Act implications for user-facing AI

## Description

* Ship AI applications end to end: discovery, design, build, evaluation, deployment, and operation.
 * Partner with functional stakeholders to identify and scope candidate applications.
 * Translate stakeholder needs into application architecture, with senior support where deeper infrastructure expertise is needed.
 * Build against the AI team's capability stack (gateway, retrieval, evaluation) rather than reinventing it.
 * Define and execute the evaluation plan, both technical and product, for each application you ship.
 * Stay close to what you've shipped: monitor adoption, evaluate continuously, and iterate based on real usage data.
 * Carry on-call responsibility for the applications you operate.
 * Represent the AI team in routine engagements with functional users and leaders, including demos, training, and adoption support.


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