Assignment Context

RSVZ is a dynamic organization where more than 150 people work within the Informatics department. RSVZ is a bilingual environment, with both French-speaking and Dutch-speaking stakeholders. The organizational culture is informal. Within IT, Agile SAFe is applied and the development teams are multidisciplinary.

At RSVZ, we want to deploy AI solutions to make knowledge more usable, improve interactions, process document flows more efficiently, and enable new digital services. The focus is on concrete AI functionality that is integrated into applications, processes, and internal systems, with particular attention to reliability, security, and manageability.

We are looking for an AI Engineer who will be responsible for building, integrating, and operationalizing AI solutions in production. The AI Engineer will specifically focus on modern AI applications such as generative AI, retrieval-based solutions, document processing, and intelligent assistance within business processes.

Role

The AI Engineer is responsible for designing, building, and integrating AI functionality within RSVZ. He/she translates use cases into usable and secure AI solutions that can function in production and connect with existing systems, data sources, and workflows.

The AI Engineer focuses on the technical realization of AI use cases within an agreed architecture and in collaboration with relevant teams. The AI Engineer does not determine the organization-wide AI roadmap, but ensures a strong implementation of concrete solutions.

The candidate possesses a thorough command of Python for the development of AI services, retrieval pipelines, and backend integrations, and a solid operational knowledge of C#/.NET to design API contracts towards the (mainly .NET-based) RSVZ applications and to collaborate smoothly with the integration and development teams. Beyond technical skills, a strong evaluation and iteration discipline is expected: systematically measuring the quality, reliability, and security of AI output (grounding, hallucinations, bias, consistency).

Key Responsibilities

Implementing AI use cases

• Translating functional needs into concrete AI solutions that are usable in applications and processes.
• Building AI functionality for use cases such as intelligent assistance, document processing, summarization, information extraction, classification, or knowledge discovery.
• Implementing solutions based on generative AI, LLMs, retrieval/grounding, and relevant AI services, preferably using open-source models and frameworks where possible (Hugging Face, LangChain, LlamaIndex, etc.)
• Taking into account reliability, error handling, security, and user experience.

Integration with systems and processes

• Designing and implementing integrations between AI components and internal applications, APIs, data sources, and document flows.
• Ensuring robust integration into existing workflows and backend processes.
• Working with access management, auditing, input/output validation, and other necessary management measures.
• Contributing to secure and maintainable implementations in collaboration with development and security teams.

Evaluation and quality assurance

• Setting up and maintaining evaluations for AI solutions, with attention to relevance, accuracy, consistency, grounding, and security.
• Developing test scenarios, regression tests, and review flows for AI functionality.
• Improving prompts, retrieval logic, output structures, and interaction patterns based on evaluation results.
• Helping ensure that AI solutions are sufficiently predictable, controllable, and usable for end users.

Productionization and monitoring

• Translating POCs and experiments into production-ready AI services.
• Setting up deployment approach, version management, and operational follow-up for AI components.
• Providing monitoring and observability for AI solutions, including logging, traces, latency, error rates and usage, inference costs (tokens).
• Identifying and improving issues related to performance, reliability, cost, and quality in production.

Collaboration and knowledge sharing

• Aligning with developers, architects, product owners, security, and business stakeholders on implementation, integration, and quality.
• Contributing to good practices regarding AI engineering, evaluation, and safe adoption of AI solutions.
• Documenting implementations, limitations, and operational points of attention.
• Sharing knowledge with teams regarding the responsible and effective deployment of AI functionality.

Behavioral

• Results-oriented and pragmatic: able to convert AI solutions into usable and reliable production functionality.
• Strong analytical and systematic thinking, with an eye for trade-offs, risks, and feasibility.
• Taking ownership of technical realizations within assigned use cases or components.
• Awareness of issues regarding compliance, ethics, and governance of AI systems, especially given the specific risks of LLMs (hallucinations, bias, data leaks).
• Strong communication skills: able to clearly explain technical choices to technical and non-technical stakeholders.
• Collaborative and easily deployable in a multidisciplinary context.
• Proactive in suggesting improvements in terms of quality, reliability, and reusability.
• Eager to learn and able to quickly translate new AI techniques into concrete applications.

Language Skills

• French or Dutch speaking
• Understanding of the second national language

Work Regime

Hybrid, specifically 2 days a week in the office and 3 days remote working