Job Overview
An ML engineer at IFS combines software engineering, DevOps, and AI/ML expertise to design and maintain scalable AI infrastructure, develop and deploy ML pipelines, and serve models at scale. Responsibilities
Design, develop, and maintain high-performance, scalable AI/ML infrastructure.
Build and execute efficient ML pipelines and serve models at scale.
Collaborate closely with data scientists, data engineers, architects, and DevOps engineers to create production-ready solutions.
Continuously monitor and improve model performance, implementing monitoring, observability, and drift detection.
Expand knowledge of AI infrastructure and share insights to guide others.
Qualifications
Strong proficiency in Python and relevant libraries (NumPy, Pandas, Kserve, etc.); knowledge of additional languages like Go, C#, or SQL is a plus.
Experience with cloud platforms (Azure) and infrastructure-as-code tools (Terraform, Helm), CI/CD, GitOps (ArgoCD), and containerization (Docker,



Kubernetes).
Foundational understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and model evaluation techniques.
Proficiency in data preprocessing, feature engineering, and experience with relational and vector databases.
Excellent analytical skills and the ability to interpret complex datasets and derive actionable insights.
Excellent verbal and written communication skills, with the ability to collaborate effectively with cross‐functional teams and stakeholders.
Bachelor's or master's degree in Computer Science, Software Engineering, Data Science, or a related field, with at least 3+ years of software development experience, including experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit‐learn.
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📌 Lead/Senior Machine Learning Engineer (Madrid)
🏢 Ifs
📍 Madrid