AI Deployment Engineer

• Machine Learning fundamentals and evaluation Solid technical knowledge of Machine Learning and Deep Learning models, with the ability to correctly formulate different types of problems and evaluate, interpret, and improve model performance based on the use case. Ability to select appropriate metrics, analyze results, define validation criteria, and make technical decisions oriented toward business and operations. __
• LLMs, GenAI, RAGs and agents
  • Technical knowledge of language models, embeddings and context management, applied to generative AI solutions in production.
  • Ability to design, evaluate and operate RAG systems, as well as to control quality, relevance and hallucinations in responses.
  • Understanding of agent-based architectures, including orchestration, tool use and memory management.
  • Application of security, privacy and control criteria in GenAI solutions.
• Software engineering (Python) applied to deployment Ability to develop and structure AI services in Python following software engineering best practices. Experience in designing and operating inference APIs, as well as managing the code lifecycle in production environments. Application of testing strategies, contract validation, and reproducible environment management, focused on
• MLOps / AIOps and infrastructure Knowledge of MLOps and AIOps to manage the complete lifecycle of models in production, from training to deployment and retirement. Ability to design and operate pipelines, model versioning, and CI/CD processes, ensuring safe and reversible deployments. Experience deploying AI
• Monitoring, drift, bias, and governance Ability to define and implement monitoring and observability strategies for AI models and services in production. Identification and management of model degradation, data drift, and behavior changes over time. Awareness and application of Responsible AI principles, including bias, fairness,

• Join a leading entity in the sector with innovative and novel projects.
• Career plan, professional development and continuous training.
• Permanent contract, flexible working hours and hybrid model.
• Competitive salary, benefits plan and flexible remuneration.

At VidaCaixa we are very clear that there is only one life. That is why we dedicate it to doing what we are most passionate about.

Shall we tell you what moves and motivates those of us who are part of the leading insurance company in Spain?

The purpose that guides us is a strong commitment to people. We work to guarantee a better future for our clients and our team. That is why we dedicate every today to thinking about tomorrow, using all the resources at our disposal to achieve it, such as digitalization or artificial intelligence.
Our will to improve the future of society is reflected in our commitment to creating inclusive products and making our insurance offering even more accessible to vulnerable groups. Furthermore, these are values very much our own: quality, the essence of everything we do, and the trust of our clients.

Join the leading insurance company in Spain, a growing sector with enormous potential. A solid and expert company, cutting-edge and innovative. The future project of more than 800 professionals.We are at a time of transformation and growth at all levels, with a clear commitment to technology and a data-driven approach, which is why we are looking for profiles like yours: curious and restless people who bring their knowledge and join our challenges.Learn and work alongside the top professionals in the sector. Progress in your career and unleash your

You will be part of the Artificial Intelligence Directorate, with the main objective of deploying, operating, and maintaining Machine Learning and GenAI solutions developed by Data Science teams. The role is focused on ensuring technical quality, scalability, observability, governance, and compliance throughout the entire model lifecycle, from prototype to controlled retirement.

• Industrialize and deploy Machine Learning, Deep Learning, RAGs, and agents models in production environments.
• Design and operate inference services (batch, online, and streaming) with defined SLAs.
• Implement training and inference pipelines, versioning, and model promotion.
• Define and implement monitoring strategies, drift detection, and quality control in production.
• Ensure end-to-end observability (logs, metrics, traces) and establish SLOs/alerts.
• Manage deployments, rollbacks, and canary/blue-green strategies.
• Integrate and operate LLM providers and external services, controlling latency, costs, and limits.
• Ensure Responsible AI, privacy (PII), and regulatory compliance (EU AI Act).