THE ROLE

As Applied AI / ML Lead Engineer, you will help build DAiNA’s applied AI capability at the core of our precision-oncology platform. Your focus will be on translating modern machine learning, large language models and retrieval-augmented generation into robust, secure and clinically relevant software systems. You will design and implement AI-driven workflows that support molecular data interpretation, clinical reporting, knowledge retrieval, decision support and digital-twin development. A key part of the role is to ensure that sensitive patient and molecular data can be processed in a secure, compliant and auditable environment, with strong control over model behavior, data provenance and system performance.

WHAT YOU’LL DO

• Design, build and operate applied AI/ML systems for DAiNA’s precision-oncology platform• Develop and maintain retrieval-augmented generation (RAG) architectures for molecular data interpretation, clinical reporting, literature/knowledge retrieval and internal decisionsupport workflows.• Build robust components for document ingestion, chunking, embedding, vector search, reranking, grounding, citation handling and evaluation.• Deploy and operate open-weight and/or self-hosted models in secure cloud or isolated environments, reducing unnecessary dependency on external AI providers for sensitive patient data.• Evaluate and fine-tune domain-specific models where this adds measurable value, using reproducible training, validation and benchmarking workflows.• Integrate AI components into DAiNA’s reporting layer, data platform and broader computational oncology workflows.• Develop guardrails, audit trails, hallucination-control strategies and human-in-the-loop review mechanisms for sensitive scientific and clinical use cases.• Lead and coordinate a small distributed technical team or external specialists across architecture, retrieval, model serving, fine-tuning and AI evaluation.• Work closely with bioinformatics, data engineering, clinical, wet-lab and management stakeholders to ensure AI systems address real workflow needs.

WHAT YOU BRING

• Strong software engineering background with hands-on experience building productiongrade ML, AI or LLM-based systems.• Practical experience with large language models, including open-weight model families• Deep practical understanding of RAG systems, including embeddings, vector databases, chunking strategies, retrieval, re-ranking, grounding and evaluation.• Experience with MLOps practices such as model/version management, automated evaluation, monitoring, logging, deployment and reproducibility.• Strong communication skills and the ability to explain technical trade-offs clearly to scientific, clinical and management stakeholders.

NICE TO HAVE

• • Experience in healthcare, biotech, diagnostics, pharma or another regulated environment.• Experience with clinical or biomedical data, including molecular profiles, omics data, pathology reports, clinical notes, literature, treatment guidelines or real-world evidence.• Familiarity with digital twin approaches, computational oncology, cancer biology or personalized medicine.