AI Native Engineer

Unilabs is one of Europe's leading diagnostics groups — 12,500 people, 200+ laboratories across 14 countries, and more than 237 million diagnostic tests performed annually. In pathology alone, Unilabs processes tens of thousands of histopathology cases each year, with flagship digital pathology centres already operating at 100% whole slide image scanning capacity in Geneva and Lausanne. Core Responsibilities Core Agentic Architecture & Retrospective Extraction LLM Extraction Agents: Design, build, and maintain production-grade LLM-based extraction pipelines to automatically parse years of unstructured PDF pathology reports. Structured Parsing: Programmatically extract clinical entities such as diagnoses, tumor grades, pathological staging, and critical biomarker statuses from raw, free-text documents. Framework Selection: Evaluate and integrate specialized agentic frameworks and orchestration tooling (e.g., LangChain, LlamaIndex, or direct LLM API implementations) based on measurable extraction accuracy against real-world clinical text, rather than what is fashionable. Confidence Scoring & Human-Review Loops: Build programmatic confidence scoring systems and human-inthe- loop validation queues that flag low-confidence extractions for clinical review based on validation parameters defined by our Clinical Informatics Lead. Multi-Modal Pipeline & Next-Gen API Infrastructure Diagnostic Data Fusion: Architect and maintain the data pipelines that link pathology LIS data with separate molecular/genetics information systems. You will ensure that vital markers like KRAS, NRAS, BRAF, MMR/MSI status, and ctDNA results seamlessly map to the exact same case record as the histology diagnosis. Interoperable Interface Engineering: Implement robust REST APIs, HL7 v2, or HL7 FHIR interfaces to feed structured pipelines directly into downstream matching layers or ecosystems like Proscia Concentriq and Aperture. Future Ecosystem APIs: Lay the architectural groundwork for secure, high-throughput API layers destined to interface with premium consumer wearables, external preventive health apps, and cloud-native hospital systems. Data Quality Observability: Develop automated data-quality monitoring systems to catch and flag anomalous outputs, missing biomarker fields, or incomplete clinical records before they touch delivery endpoints. Governance, De-Identification & Compliance Anonymization Infrastructure: Implement technical de-identification protocols to securely strip or pseudonymize direct and indirect patient identifiers. Regulatory Alignment: Technical execution must align completely with strict health data privacy guardrails across global and regional frameworks, including the Swiss nDSG and EU GDPR Article 9. Lineage Tracking: Build exhaustive audit logging and data lineage tracking for every clinical record processed, preserving clinical data provenance for pharma and clinical partner credibility. AI Native & Agentic Mindset LLM Engineering Pro: Practical, hands-on experience utilizing LLM APIs, building system prompt state machines, and fine-tuning prompt engineering for highly structured text-extraction tasks. Agent Infrastructure Fluency: Direct experience working with agentic frameworks (LangChain, LlamaIndex, or equivalent custom graph state setups) to orchestrate complex, multi-step clinical data transformation workflows. Production Focus: You have shipped non-deterministic models into production environments and understand how to manage context windows, token costs, rate limits, and output evaluation metrics. Core Software Engineering & Stack Experience Backend Proficiency: 4–7+ years of core software engineering experience with deep mastery of Python and SQL, capable of debugging asynchronous, multi-step pipelines independently. Regulated API Design: Deep familiarity with constructing and consuming production-grade REST APIs within highly regulated or clinical environments. Cloud & Containerization: Practical deployment experience across cloud infrastructure providers (AWS, Azure, or GCP) utilizing Docker containerization. Data Standards (Highly Preferred): Working knowledge of clinical health standards like HL7 v2, FHIR, or relational data models such as OMOP CDM and CDISC conventions. Data Formats (A Plus): Exposure to digital pathology data formats (DICOM, whole slide image file formats like SVS and NDPI), or LIS systems. Working Environment Expectation AI-Assisted Workflow: We build with modern tooling. You are expected to comfortably utilize AI-assisted environments like Cursor, GitHub Copilot, or equivalent editors as an active force multiplier to accelerate problemsolving. We care about what you ship, not how many characters you manually typed. What We Offer Hybrid working model ( office & remote flexibility) International, collaborative, and regulated product environment Competitive compensation and benefits Long-term ownership of a strategic healthcare product The Ultimate Unfair Data Moat: Direct engineering access to Europe's largest diagnostic pool—combining deep Pathology, Imaging, and Blood tests across millions of real, longitudinal patient journeys. No Toy Problems: The opportunity to move past generic chatbot wrappers and deploy agentic AI that directly impacts precision clinical trial execution, therapeutic drug development, and global preventative longevity markets. True Entrepreneurial Ownership: The execution speed, raw ownership, and equity upside of a venturebacked standalone seed-stage company, powered by the structural footprint of Unilabs and A.P. Møller Holding. #J-18808-Ljbffr