Associate AI Engineer

About the role

A growing team at IQVIA is looking for a motivated, early-career Associate AI Engineer to help build the AI solutions supporting its Health Economics and HTA team. Working under the guidance of senior engineers, the Associate AI Engineer turns validated prototypes into production-grade AI tools, supports new prototypes with delivery teams and helps maintain existing tools. The role covers the full AI lifecycle, from retrieval and data extraction to drafting and quality control, within an Agile team across geographies.

Key responsibilities

• Production: rebuild validated prototypes as production agents on the enterprise AI development platform, write reusable pipeline modules (retrieval, screening, data extraction, drafting and quality control), write tests and evaluation harnesses following enterprise standards.
• Prototyping: build and refine AI workflows with delivery teams, run pilots on live projects and measure time saved.
• Support: handle routine runs and change requests for existing AI tools, package applications for deployment.
• Working ways: work in Agile sprints, manage backlog and tickets in Jira, raise merge requests and act on review feedback before shipping.

Required profile

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Informatics, IT or Data Science; a Master's in AI, ML or Data Science is preferred.
• 3-4 years of industry experience, including 2-3 years in AI/ML-related roles.
• Fluent English and strong technical writing and documentation skills.
• Ability to explain technical topics to business stakeholders and subject-matter experts, and to work independently while taking ownership of deliverables.
• Desirable: exposure to the healthcare sector or pharmaceutical market, and experience in a regulated industry (pharma, healthcare, finance).

Required skills

• Advanced Python with Git, CI/CD, unit, integration and end-to-end testing (e.g. pytest) and REST APIs (e.g. FastAPI).
• Hands-on LLM application development: RAG, tool calling, agent workflows and LLM APIs (Azure OpenAI, Anthropic, AWS Bedrock).
• Natural language processing and information extraction from unstructured documents.
• Relational databases and SQL (e.g. PostgreSQL), workflow automation (Power Automate), and agentic AI tools (GitHub Copilot, Cursor, Claude Code).
• Agile delivery with Jira and merge-request-based code review.
• Desirable: computer vision for documents, agent orchestration frameworks (LangGraph), LLM evaluation tools (LangSmith, Langfuse), Docker and Linux.