Senior Full Stack Developer (AI-Native)

As a Senior Full Stack Developer, your goal will be to participate in the development of our new issuance monitoring and management application, as well as our Distribution, Pricing and Processing platform.

We are looking for an AI-native engineer with a strong software engineering mindset, capable of leveraging modern AI tools and practices across the entire Software Development Life Cycle (SDLC), from requirements analysis and solution design to development, testing, deployment, monitoring, and continuous improvement.

You will work in a collaborative Agile environment, contributing to the design and implementation of scalable, secure, and high-quality enterprise applications.

Responsibilities

Participate in the design, development, and maintenance of innovative enterprise applications.

Collaborate with business stakeholders and technical teams to analyze requirements and define technical solutions.

Design and develop new application modules and microservices-based solutions.

Develop and maintain backend and frontend components using modern technologies and frameworks.

Create and execute test cases, ensuring software quality and reliability.

Participate in CI/CD processes, deployments, and production support activities.

Diagnose and resolve incidents, performance bottlenecks, and application issues.

Contribute to architecture discussions, technical decisions, and best engineering practices.

Use repositories, DevOps pipelines, and software development tools efficiently.

Promote clean code, maintainability, observability, and security best practices.

Mentor junior developers and contribute to a strong engineering culture.

AI-Native Engineering Responsibilities

Leverage AI-assisted development tools throughout the SDLC to improve productivity, quality, and delivery speed.

Use AI coding assistants and autonomous engineering tools such as:

Devin

GitHub Copilot

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ChatGPT

Claude

Gemini

Sourcegraph Cody

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Apply AI-driven approaches for:

Code generation and refactoring

Automated documentation

Test generation and validation

Debugging and root cause analysis

Code reviews and quality improvements

SDLC automation and developer productivity optimization

Understand the capabilities, limitations, and appropriate usage of Large Language Models (LLMs) in software engineering workflows.

Collaborate in the adoption of AI engineering best practices, governance, and secure usage of AI tools in enterprise environments.

Stay up to date with emerging AI engineering trends, frameworks, and development accelerators.

Mandatory skills:

5+ years of experience working as a Full Stack Software Developer / Software Engineer (backend-oriented preferred).

• Strong experience with:

Java; Spring Boot; Hibernate / JPA

• Experience with frontend technologies such as:

Angular; HTML; CSS or JavaScript / TypeScript

• Experience with relational databases and SQL technologies such as Oracle and PostgreSQL.

• Experience designing and consuming REST APIs.

• Experience with Git, GitHub, and collaborative development workflows.

• Experience working with Microservices Architecture.

• Experience with Jenkins and CI/CD pipelines.

• Experience with Agile frameworks (SCRUM).

• DevOps & Cloud

• Experience with containerization technologies such as Docker and OpenShift.

• AI & Modern Engineering Skills

• Hands-on experience using AI-powered development tools in real software engineering workflows.

• Understanding of prompt engineering and AI-assisted development practices.

• Experience in integrating AI into engineering processes and delivery pipelines

• Spanish and English level C1

Soft skills:

• Strong problem-solving, analytical, and communication skills.

• Passion for innovation, automation, and continuous improvement.

Nice to Have

• Experience with event-driven architectures and messaging systems.

• Experience with Kubernetes.

• Experience with automated testing frameworks.

• Exposure to AI/ML platforms or GenAI integrations in enterprise applications.

• Knowledge of software security and secure coding practices in AI-assisted development environments

• Experience with cloud environments such as AWS is a plus.

• Knowledge of monitoring, logging, and observability tools is a plus.