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.

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).