Ai Software Engineer

Randstad; Madrid; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Jungheinrich is seeking an experienced AI Software Engineer to advance AI-powered software development within its central IT. The role focuses on creating scalable AI-enabled applications, integrating AI into development platforms, and driving productivity with agents, MCP, and DevSecOps practices.

Key responsibilities include building AI-assisted workflows, designing reusable AI agents, and collaborating with platform teams to establish secure, scalable development environments.

Formación

• Design scalable AI-enabled applications and services aligned with business goals.
• Strong understanding of modern software architecture, API design, and modular patterns.
• Experience with Git-based workflows, PRs, branching strategies, and code reviews.

Responsabilidades

• Implement AI-assisted software development workflows across the SDLC.
• Design and maintain AI-enabled apps and services.
• Integrate AI capabilities into development platforms and CI/CD pipelines.

Conocimientos

AI architecture

Software architecture

GitHub workflows

Prompt engineering

AI agents

MCP ecosystem

DevSecOps

Herramientas

GitHub

MCP servers

Descripción del empleo

To strengthen AI-powered software development within the central IT of Jungheinrich, we are looking for an experienced and hands-on AI Software Engineerwho combines software engineering excellence with deep practical knowledge of Generative AI, AI-assisted software development, and modern developmentplatforms.

The primary objective of the role is to significantly increase development productivity through the effective use of modern AI technologies (e.g., GitHubCopilot, AI Agents, MCP, AI-driven testing and DevSecOps solutions) while establishing the necessary platforms, processes, standards, and governancestructures.

This role combines software architecture, AI engineering, platform engineering, and hands-on software development. The position holder shapes the futureof AI-enabled software development within Jungheinrich and actively supports ongoing development initiatives through the direct application of modern AItechnologies.

Key Responsibilities:

• Implement AI-assisted software development workflows across the SDLC.
• Design, develop, and maintain AI-enabled applications and services.
• Integrate AI capabilities into existing development platforms.
• Support teams in utilizing AI for coding, testing, documentation, reviews, and troubleshooting
• Design and implement AI Agents for software engineering use cases.
• Define requirements for Model Context Protocol (MCP) servers and integrations
• Collaborate with platform teams to provide scalable AI development environments.
• Define agent orchestration patterns and reusable agent architectures.
• Create training materials, playbooks, patterns, and best practices.
• Support integration into CI/CD and DevSecOps pipelines.
• Contribute to the evolution of AI-enabled Internal Developer Platforms (IDP)

Requirements:

• AI Solution Architecture: Strong ability to design AI-enabled applications, services, and automation solutions that are scalable, secure, maintainable, and aligned with business objectives.
• Software Architecture & Development Principles: Strong understanding of modern software architecture, API design, modular application design, reusable development patterns, and long-term maintainability.
• GitHub: Strong experience with Git-based workflows, pull requests, branch strategies, code reviews, repository governance, and collaboration in distributed development teams.
• Prompt Engineering: Ability to design structured prompts, context strategies, and interaction patterns that enable AI systems to generate reliable, highquality, and reproducible results.
• AI Agents: Ability to design, implement, and orchestrate reusable autonomous or semi-autonomous AI agents that can plan tasks, make decisions, and interact with external systems.
• Skill Engineering: Ability to model, define, and maintain reusable AI skills, tools, and knowledge capabilities that can be consumed and executed by AI systems.
• MCP Ecosystem: Ability to design and implement Model Context Protocol (MCP)-based integrations that provide AI systems with secure, standardized access to enterprise data, applications, and services.
• Token Economy & Cost Management: Ability to understand and optimize token usage, model selection, context window design, caching strategies, and AI service consumption to ensure cost-efficient, scalable, and value-driven use of Generative AI solutions.

Nice to have:

• Programming and Scripting: Experience with Python, TypeScript, PowerShell, Bash, or comparable languages for automation and integration tasks.
• Observability: Knowledge of monitoring, logging, tracing, and operational dashboards for cloud-native applications.