Principal Innovation in AI Engineering and Harness

Demonstrated technical experience is required, knowing all phases of the code development lifecycle using AI agents. Knowledge of agentic development tools such as Claude Code or Codex will also be valued.

• Having worked in AWS and GCP
• Understanding the key elements of architectures that support AI developments and solutions
• Having built some agentic harness for Generative AI projects and solutions.
• Having worked with tools such as Claude Code
• Having developed some application or prototype using these types of tools such as Claude Code or Codex.
• Experience deploying AI-developed applications on container infrastructure (Kubernetes).
• Demonstrated experience in innovation projects and practical application of AI.
• Select and comparatively evaluate foundational models for different use cases, including language and voice applications 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.

Development of solutions based on LLMs and agentic systems

• Design and develop analytical solutions based on LLMs, including architectures that combine several models.
• Design and develop agentic systems in which models interact with tools, APIs, external knowledge sources, and ML models.
• Develop reasoning flows and model orchestration, with task planning, tool use, and composition of several inference steps.
• Build knowledge bases and domain datasets to feed generative models.
• Create systems where LLMs function 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 advances in LLMs, foundational models, advanced NLP techniques, and agentic architectures.
• Evaluate technologies and frameworks relevant to generative AI solutions.
• Communicate technical concepts to internal audiences with different levels of specialization.
• Experience in the full lifecycle of ML and NLP projects: exploratory data analysis (EDA), dataset construction, model selection and training, evaluation, and monitoring.
• Experience developing productive analytical solutions, ML pipelines, and inference services.
• Experience in MLOps: pipeline automation, continuous integration and deployment, observability, and model 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 other equivalents.
• Experience using programming agents such as Codex, Claude Code, Cursor, GitHub Copilot, or similar tools.

• Experience in cloud companies such as 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.

English Language, Machine Learning (ML), Object-Oriented Programming (OOP), Tensorflow