G

Getnet

Provides payment processing for merchants worldwide

AI Engineer - Software Delivery Enablement

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBoadilla del Monte, Madrid, Spain

About the Job

Requirements

  • A strong background in software engineering, software development lifecycle practices, DevOps, quality assurance, platform engineering, or engineering productivity is required.
  • Hands-on experience designing or implementing AI-based solutions using large language models, prompt engineering, agents, retrieval-augmented generation, workflow automation, or developer productivity tools is required.
  • A solid understanding of end-to-end software delivery, including Agile delivery, backlog refinement, requirements management, development, testing, continuous integration and continuous delivery, release management, and production support is required.
  • Experience creating reusable AI skills, prompts, agents, templates, automations, or delivery accelerators, and integrating them with application programming interfaces, repositories, continuous integration and continuous delivery pipelines, ticketing systems, documentation platforms, or internal tools is required.
  • Professional certifications in AI, software engineering, cloud technologies, or Agile/Scrum are preferred.
  • Knowledge of large language models, prompt engineering, AI agents, retrieval-augmented generation architectures, workflow automation, and model evaluation techniques is required.
  • Programming or scripting experience with Python, JavaScript, TypeScript, Java, or a similar language is required.
  • Experience integrating application programming interfaces with repositories, continuous integration and continuous delivery pipelines, ticketing systems, documentation platforms, internal tools, and enterprise AI services is required.
  • Knowledge of software engineering practices covering version control, testing, documentation, monitoring, observability, secure coding, and maintainability is required.
  • Knowledge of software quality, traceability, testing strategies, delivery governance, and defining metrics for AI adoption and workflow impact is required.
  • Knowledge of embeddings, vector databases, AI observability, prompt and model evaluation, hallucination mitigation, and human-in-the-loop validation is preferred.
  • Experience with tools such as GitHub, GitLab, Bitbucket, Jira, Confluence, Jenkins, Azure DevOps, SonarQube, QTest, ServiceNow, or similar tools is preferred.
  • Knowledge of enterprise architecture standards, delivery frameworks, and technology governance models is preferred.
  • Analytical thinking and structured problem-solving are required.
  • Clear communication with technical and non-technical stakeholders is required.
  • Cross-functional collaboration and stakeholder management are required.
  • A pragmatic, outcome-oriented mindset focused on measurable improvements is required.
  • Ownership and accountability for solution quality are required.
  • The ability to enable adoption and facilitate change across multidisciplinary teams is preferred.
  • Continuous improvement and learning agility are preferred.
  • English is required; Spanish is preferred.

Responsibilities

  • Enable AI-assisted software delivery across the full lifecycle, from ideation and discovery through requirements, design, development, testing, release, deployment, production support, and post-release analysis.
  • Translate business ideas and product needs into structured delivery artifacts, including user stories, acceptance criteria, technical requirements, test scenarios, documentation, release notes, and production-readiness evidence.
  • Identify friction points, repetitive work, and quality gaps across the software development lifecycle and turn them into practical AI-assisted workflows and automations.
  • Design, build, maintain, and improve reusable AI skills, agents, prompts, workflows, templates, and delivery accelerators for engineering teams.
  • Integrate AI capabilities with internal tools, documentation platforms, repositories, ticketing systems, continuous integration and continuous delivery pipelines, quality platforms, and enterprise knowledge bases.
  • Apply software engineering practices to ensure AI solutions are robust, reusable, scalable, auditable, observable, secure, and maintainable.
  • Define practical guardrails and validation controls, partnering with Security, Risk, Compliance, Architecture, and Governance teams to support safe and responsible AI adoption.
  • Measure adoption and business impact through reduced manual effort and cycle time, stronger documentation and test coverage, better traceability, fewer delivery issues, and improved developer experience.

About the Company

Getnet

Provides payment processing for merchants worldwide

Getnet provides payment solutions for merchants of all sizes, including POS terminals, e-commerce payment gateways, and mobile payments. It processes transactions for a fee and sells or rents POS devices, while offering value-added services like fraud prevention, risk management, and data analytics accessed through the Getnet Portal. The platform combines hardware, software, and analytics to help merchants manage sales and gain insights across regions. Backed by PagoNxt (Banco Santander), Getnet leverages a broad bank-backed fintech network to serve Latin America and Europe, aiming to help merchants streamline payments, reduce risk, and grow.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Madrid, Spain
Founded
2009

Recently Posted Jobs

Explore more opportunities at Getnet

See more jobs