**MANDATORY FOR CONSIDERATION: YOUR CV MUST INCLUDE A WORKING LINK TO YOUR PORTFOLIO OF WORK (FOR EXAMPLE GITHUB). APPLICATIONS WITHOUT A PORTFOLIO LINK WILL BE DECLINED AT SCREENING.
Company Description**
We design and build Agentic science-based diagnostic and development systems that sit at the intersection of AI, Science and Psychology. We digital twins, multi-agent orchestration systems and Ai driven diagnostics assessment systems to measure human psychological states, model individual development trajectories, and deliver hyper-personalised interventions grounded in validated theory and psychometric evidence. Every system we build is designed for auditability, EU AI Act compliance, and real-world clinical and professional use.
We work with major financial services, technology and health-care enterprise clients. Our team spans applied AI engineering, psychometrics, psychology, and data science.
Role Description
We are looking to expand our team with two additional Generative AI Engineers to help build the next generation of psychological assessment systems. The systems we are building uses a graph-based orchestration architecture with a governed state store, retrieval-grounded intervention delivery, and a digital twin layer that models individual psychological profiles through non-intrusive measures over time. We are at an advanced stage of development. You will not be starting from scratch.
We have two positions open. Both are engineering roles with significant depth requirements. One position has a stronger emphasis on the assessment and intervention delivery layer. The other is focused on the digital twinning architecture. You will work closely with AI systems psychologists. Day-to-day tasks will include designing, implementing and evaluating multi-agent architectures, assisting with fine-tuning machine learning models, collaborating with multidisciplinary experts, and utilizing cutting-edge AI techniques to create impactful, science-based psychological assessment and development tools.
What You Will Do
- Build graph or state-machine orchestrator agents that enforces a structured multi-step workflow with persistence and resumability for long-running interactions and handoffs.
- Implement RAG pipeline for a curated content and intervention library, including ingestion, chunking, metadata design, hybrid retrieval, reranking, and provenance.
- Design scoped context layer that exposes only policy-approved, banded user state to the model while keeping the user experience seamless.
- Implement safety controls and escalation flows aligned to modern LLM threat models, including prompt injection and sensitive data leakage risks.
- Instrument the system for evaluation, monitoring, and regression testing so changes do not degrade safety or retrieval quality.
- Fine-tune Llama-based models for psychological state, and trait classifications
- Additional engineering tasks appropriate to the nature and scope of work from our clients
Qualifications
- Master’s degree or higher in Computer Science, AI, ML, NLP, Data Science, Statistics, or equivalent
- Demonstrable experience building multi-agent or tool-using LLM systems with an orchestrator, ideally graph-based tool calling, structured outputs, and testing
- Strong skills in Machine Learning, Artificial Intelligence, and Natural Language Processing
- Proficiency in programming languages such as Python, R, and experience with deep learning frameworks like TensorFlow or PyTorch as well as Langchain/Langraph
- Proficiency in API design and datamodelling (SQL)
- Hands-on experience with developing RAG systems (using LlamaIndexing) and vector retrieval systems (pgvector, Qdrant, Pinecone, Weaviate, or similar)
- Experience in fine tuning and deploying generative AI models like RoBERTA and Llama 4.2
- Ability to create data-driven solutions with expertise in data preprocessing, analysis, and feature engineering
- Strong communication and collaboration skills for working in interdisciplinary teams
- Familiarity with ethical AI principles and responsible AI development practices
- Llama Guard style classifier pre and post generation, plus hard-coded handoff flows, designed against OWASP LLM risk
- Strong security mindset for LLM applications, including practical mitigations for prompt injection and data exfiltration risks.
- Fully Proficient in English.
- **NOTE: MUST BE LEGALLY ALLOWED TO WORK IN OR CONTRACT TO EUROPE
Nice to have**
- Familiarity with psychometrics, assessment design, or measurement concepts
- Some understanding of psychology, organizational psychology, or applied behavioural science
- Data science experience, especially experimentation, evaluation, and monitoring
- Familiarity with GenAI risk management practices such as NIST’s GenAI profile guidance
Contract Details
- Initial 12 month contract, with a strong likelihood of permanent employment thereafter.
- Remote, Europe-based, priority for strong overlap with CET and EET working hours
- Full-time
Application Process
The selection process has three stages:
- Portfolio and CV review. We will assess your portfolio and application materials before scheduling any conversations.
- Technical interview. A structured conversation with our Chief Solutions Architect. Expect questions on architecture decisions, failure modes, and how you think about safety in psychologically sensitive contexts.
- Technical project and assessments. Shortlisted candidates will complete a time-limited technical exercise and a set of assessments relevant to the role.
Portfolio submission requirements
Applications must include a portfolio with at least two of the following:
- An orchestrated agent workflow (graph or state machine)
- A production RAG system (retrieval design, metadata, evaluation)
- A concrete example of an LLM safety mitigation you implemented (threat model, controls, testing)
- Describe a graph-based orchestration you implemented and how you handled retries, persistence, and evaluation
How to Apply
Submit your CV (with three contactable references), portfolio links, a short note describing one relevant system you built (architecture, what failed, what you changed), and your availability and rate expectations to [email protected] with Reference (GENA-0129A12) in the title.
We read every application personally. If your portfolio is strong and your experience aligns with what we are building, we will be in touch within five working days.
Job Types: Full-time, Contract
Contract length: 12 months
Pay: €3.400,00 - €5.000,00 per month
Application Question(s):
- Does your CV provide a link to your portfolio (GitHub, demo, or technical write-up) showing an orchestrated agent system, plus a short note pointing to the most relevant repo or section?
- Have you built a multi-agent LLM system with a central orchestrator (single user-facing persona, hidden specialists) and is there evidence of such in your portfolio link?
Education:
- Master's (Preferred)
Work authorization:
- Nederland (Required)
Work Location: Remote