Machine Learning Engineer

IQVIA

Romania · Full-time · Entry

About The Role

This is a hands-on engineering role focused on building and deploying production-grade AI solutions in the life sciences domain. You will work on scalable implementations of AI/ML models, transforming prototypes into robust solutions that deliver real impact for global healthcare clients.

What You’ll Do

Design, implement, and optimize end-to-end ML pipelines for healthcare applications.
Work on a variety of AI/ML projects, including opportunities to apply generative AI and foundation models where relevant.
Build and maintain production-ready algorithms that handle complex, high-dimensional data.
Collaborate with cross-functional teams to translate business needs into scalable AI solutions.
Ensure model performance, reliability, and compliance for deployment in regulated environments.

What We’re Looking For

2–4 years of experience delivering ML solutions in a commercial or production environment (not just academic projects).
Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow).
Hands-on experience with large datasets and cloud-based deployment (AWS, Azure, GCP).
Familiarity with modern ML techniques and interest in emerging technologies such as generative AI and foundation models.
Degree in Computer Science, Machine Learning, or related field.
Ability to work in a fast-paced environment and bridge technical and business requirements.

Nice to Have

Experience with MLOps tools (Docker, Kubernetes, MLflow).
Knowledge of life sciences or healthcare data

IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com

Key Skills

Ranked by relevance

ai machine learning kubernetes prototypes pytorch python docker cloud mlops aws

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