Artificial Intelligence Engineer
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- Implementing AI services, agents, retrieval solutions, and API integrations.
- Building reusable components for model access, prompting, context management, evaluation, logging, cost tracking, and error handling.
- Setting up automated test and evaluation sets for quality, reliability, security, performance, and cost.
- Applying CI/CD and MLOps / LLMOps practices for version control, deployment, configuration, monitoring, and rollback.
- Providing technical support for sandboxes and proofs of concept.
- Making AI production-ready according to architecture, security, and governance requirements.
- Supporting data and platform teams with privacy-safe test data, data flows, and connections to existing systems.
- Maintaining technical documentation, runbooks, and examples so that other teams can reuse solutions.
- Analyzing operational issues and implementing improvements based on logs, evaluations, feedback, and usage data. Technical competencies
- Experience as a software engineer, platform engineer, ML engineer, or similar hands-on role. (5-8 years)
- Strong programming skills, preferably Python and Java
- Experience with APIs , authentication, containers, and automated testing.
- Practical knowledge of LLMs , embeddings, retrieval, agents, model APIs , and evaluation methods.
- Experience with CI/CD , Git , infrastructure as code, observability, and cloud or container platforms.
- Attention to security, privacy, documentation, and operational reliability.
- Knowledge of the public sector, G-Cloud, or OISZ context is an asset. Generic competencies
- Innovation : You follow the latest trends and experiment with innovative AI technologies.
- Problem-solving ability : You analyze complex issues and translate them into pragmatic solutions.
- Collaboration : You work smoothly with colleagues from various disciplines (IT, business, security, architecture).
- Communication : You can clearly explain technical concepts to both technical and non-technical stakeholders.
- Advising : You excel at structuring, visualizing, advising, and guiding multidisciplinary decision-making.
- Results-oriented : You monitor deadlines and ensure high-quality deliverables.
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