Principal Innovation in AI Engineering and Harness

Are you excited about growing your career? BBVA is a global company with over 160 years of history operating in more than 25 countries where we serve over 80 million clients. We are more than 121,000 professionals working in multidisciplinary teams with diverse profiles such as finance experts, legal experts, data scientists, developers, engineers, and designers. Learn more about the area: AI Innovation, within AI Transformation, is responsible for knowing and exploring new technologies, models, and Artificial Intelligence solutions that can have an impact and adoption by BBVA. Since the creation of Robots and the definition of The Frame as a framework and key enabler for AI scalability, a great effort is required in research, innovation, and strategic supervision and advisory within the organization. Within AI Innovation, we work very actively with other key teams such as AI Tech, AI Governance, Data for AI, and Engineering. About the position Position Mission: We are looking for a person for the position of Principal Innovation in AI Engineering and Harness, with a solid background in cloud architectures, in the design, development, and implementation of technological solutions based on AI and/or ML, as well as experience in developing essential technological elements and assets (harness) for building and scaling Artificial Intelligence solutions. Demonstrated technical experience is required, knowing all phases of the code development lifecycle using AI agents. Experience with agent development tools such as Claude Code or Codex will also be valued. Main Responsibilities: Experience in Cloud Environments and Architectures:

• Have worked in AWS and GCP
• Understand the key elements of architectures that support AI developments and solutions
• Have built an agent harness for Generative AI projects and solutions.

• Have worked with tools like Claude Code
• Have developed an application or prototype using these types of tools like Claude Code or Codex.
• Experience deploying applications developed with AI in containerized infrastructure (Kubernetes).

• Demonstrated experience in innovation projects and practical application of AI.
• Select and comparatively evaluate foundational models for different use cases, including language, voice, and multimodal solutions.
• Design and execute evaluation plans, defining technical and business metrics and comparing models in different scenarios.
• Iteratively build training, validation, and evaluation datasets.
• Adapt generative and discriminative models through fine-tuning or other equivalent techniques.

• Design and develop analytical solutions based on LLMs, including architectures that combine multiple models.
• Design and develop agent systems where models interact with tools, APIs, external knowledge sources, and ML models.
• Develop reasoning and model orchestration flows, with task planning, tool usage, and multi-step inference composition.
• Build knowledge bases and domain datasets to feed generative models.
• Create systems where LLMs act as intelligent interfaces to interact with corporate systems and data.
• Develop inference pipelines and backend services; integrate models with the bank's systems and operations, including APIs and corporate systems.
• Participate in iterative experimentation cycles, from prototype to production solution.

• Continuously explore advancements in LLMs, foundational models, advanced NLP techniques, and agent architectures.
• Evaluate relevant technologies and frameworks for generative AI solutions.
• Communicate technical concepts to internal audiences with varying levels of expertise.

• Experience in the complete lifecycle of ML and NLP projects: exploratory data analysis (EDA), dataset construction, model selection and training, evaluation, and monitoring.
• Experience developing production analytical solutions, ML pipelines, and inference services.
• Experience in MLOps: pipeline automation, continuous integration and deployment, model observability and monitoring.

• Knowledge of Infrastructure as Code (IaC), for example CloudFormation.
• Experience with Git and CI/CD practices for software integration and deployment.
• Programming in Python; experience with TypeScript or Node.js will be valued.
• Experience with LLM and agent orchestration frameworks, such as LangGraph, Agents SDK, AWS Strands, or equivalent.
• Experience using programming agents like Codex, Claude Code, Cursor, GitHub Copilot, or similar tools.

• Experience in cloud companies like AWS or Google.
• Experience in Engineering and/or IT teams.
• Experience in on-premise environments.
• Experience in the banking sector, as part of the staff or in consulting.

• Team spirit and ability to work collaboratively.
• Ability to explain technical concepts to non-technical profiles.
• Analytical mindset and orientation towards experimentation. Flexibility and adaptation to evolving technological environments.