AIML Engineer, Agentic AI COA Accelerator

Madrid, Spain

Lisbon, Portugal

Athens, Greece

Warsaw, Poland

Barcelona, Spain

Budapest, Hungary

Full time

R1554007

IQVIA provides scientific services spanning clinical trials, real world evidence, and consulting in all areas of the product lifecycle. Our Clinical Outcomes Assessments (COAs) organisation leads the industry in generating data to ensure that the patient voice is incorporated into the development and commercialisation of medication and other drug/non-drug interventions.

We focus on understanding and meeting the needs of our clients – mostly life science/pharmaceutical companies – through the application of broad consulting expertise and technical scientific knowledge to conduct scientifically rigorous research. This research is broad and includes qualitative, quantitative, and passive approaches to understand patient, caregiver, and healthcare professional experiences and expectations of disease and treatment.

To further scale the impact of IQVIA’s COA expertise, the COA Accelerator ecosystem is expanding its AI-enabled capabilities, including tools designed to support evidence-backed COA strategy development, instrument selection, endpoint planning, regulatory precedent review, and expert decision support. These capabilities are intended to transform curated COA knowledge, regulatory precedent, scientific literature, clinical trial evidence, and internal consulting expertise into reliable AI-enabled workflows for internal and external users.

To meet our client expectations and retain the excellent reputation built up over time, the IQVIA COAs team is committed to recruiting, training and supporting driven individuals who have life science, consulting, product development, data, and/or artificial intelligence skills that can be applied to COA research and technology-enabled offerings.

Individuals joining us are assured of a rewarding and progressive career in patient-focused research. You’ll have the opportunity to address challenging client issues, across multiple geographies, with a hands-on influence in developing and delivering innovative solutions. We operate in a truly multi-cultural, collegial and collaborative work environment that is rich in development and growth.

Role & Responsibilities

• Design, develop, and optimise AI capabilities that support COA strategy generation, evidence synthesis, recommendation development, and expert review workflows.

• Own the technical implementation of AI reasoning, retrieval-augmented generation, model orchestration, prompt architecture, evaluation pipelines, and domain adaptation for COA-related use cases.

• Build AI workflows that can interpret therapy area, indication, target product profile, study phase, endpoint objectives, target population, regulatory context, and trial design considerations.

• Develop AI capabilities that generate structured, evidence-backed COA strategy recommendations, including recommended instruments, endpoint considerations, rationale, evidence gaps, and supporting source material.

• Design and optimise retrieval pipelines across structured and unstructured sources, including COA libraries, psychometric evidence, scientific literature, regulatory labels, HTA documents, clinical trial records, and internal consulting outputs.

• Implement semantic search, hybrid search, metadata filtering, reranking, source attribution, and citation-supporting workflows.

• Evaluate when to use foundation models, fine-tuned models, smaller specialist models, embeddings, rerankers, deterministic rules, or hybrid approaches.

• Develop mechanisms that allow AI outputs to distinguish between strong evidence, weak precedent, outdated information, unsupported claims, and areas requiring expert judgement.

• Create and maintain model evaluation frameworks to assess factuality, retrieval relevance, citation accuracy, recommendation consistency, clinical reasoning quality, and hallucination risk.

• Partner with COA scientists, product managers, data engineers, software engineers, security teams, legal stakeholders, and commercial teams to ensure AI capabilities are scientifically credible, secure, explainable, and commercially useful.

• Support demos, prototypes, pilots, and client-facing proof-of-concept work where AI functionality needs to be explained clearly to scientific, commercial, or technical audiences.

• Document model behaviour, assumptions, known limitations, evaluation results, decision logic, and change history.

• Contribute to AI governance practices for responsible AI use in clinical research, COA strategy, and regulated decision-support contexts.

Skills & Qualifications

• Degree in computer science, machine learning, artificial intelligence, data science, com