AI Learning Technology Specialist - Temporary

IESE's Learning Innovation Unit (LIU)

The Learning Innovation Unit (LIU) accelerates IESE's educational excellence by shaping the future of learning through designing, testing, and scaling innovative learning experiences that enhance impact for participants, faculty, and organizations.
We explore emerging pedagogies, technologies, and partnerships to build the capabilities, methodologies, and design practices that will shape IESE's future of learning. Through close collaboration with faculty and program teams, we co-create new learning formats, support program design and redesign, and provide expert advisory on learning innovation opportunities.
As IESE's central hub for educational innovation, we curate and share best practices, facilitate knowledge exchange, and enable the adoption of proven tools and techniques across programs and campuses.

We are looking for a Learning Technology Specialist who combines hands-on technical skills with strong communication and a genuine interest in education and learning.

This role supports the AI & Learning Technology area within LIU (focused on AI-enabled learning platforms, tools, and delivery) by contributing to both technical execution (building, prototyping, and operationalizing AI-enabled learning solutions) and faculty and community enablement (supporting adoption, training, and the AI Community of Practice).

The ideal candidate is technically capable (able to write code, build prototypes, and work with AI tools), but also comfortable working directly with faculty, explaining technical concepts to non-technical audiences, and contributing to workshops, pilots, and enablement sessions.

As part of the Learning Innovation Unit, the Learning Technology Specialist contributes to a growing portfolio of AI-enabled learning experiences for IESE Degree and Executive Education programs.

Main responsibilities

Build and iterate on prototypes for AI-enabled learning tools, simulations, and platforms in alignment with defined technical standards, priorities, and delivery guidelines.
Convert pilots into scalable, delivery-ready assets: documentation, reusable components, templates, and support materials.
Prepare and support controlled pilots and live sessions with program teams and faculty (setup, dry runs, troubleshooting, real-time support).
Implement reliability and quality improvements (testing, monitoring, incident response where needed).
Support technology scouting and evaluation by testing emerging tools, documenting findings, and contributing to feasibility assessments.
Support basic data analytics and instrumentation for AI-enabled learning experiences, including data collection, structuring, exploratory analysis, and preparation of insights to inform learning design, evaluation, and iteration.
Create and maintain standardized toolkits, guides, and resources for faculty adoption of AI-enabled teaching formats.
Support faculty-facing AI enablement initiatives and communities through preparation of technical assets, hands-on demonstrations, and participation in enablement sessions.
Provide operational guidance, training, and just-in-time support to faculty and staff around approved AI tools, learning technologies, and established best practices.
Capture and document learnings from pilots and faculty interactions to improve future enablement materials and processes.

Technical competences

Proficiency in coding (e.g., Python, JavaScript, or similar) to build prototypes, automate workflows, and evaluate technical feasibility.
Familiarity with AI technologies (large language models, prompting, evals) and willingness to learn rapidly in this space.
Ability to work with APIs, web applications, and data pipelines at a hands-on level.
Basic proficiency in data analytics workflows (e.g., working with datasets, exploratory analysis, simple visualizations).
Basic understanding of EdTech platforms, learning management systems, and integration standards (e.g., LTI, xAPI) is a plus.
Awareness of data privacy considerations relevant to educational technologies (GDPR, consent, data minimization).

Qualifications & Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Educational Technology, or a related technical field.
3–5 years of experience in tech roles, learning technology, applied AI, or adjacent technical roles (internships and project portfolios count).
Demonstrated ability to write code and build working prototypes (portfolio or examples welcome).
Experience working with data (e.g., logs, interaction data, usage metrics) is a plus.
Strong communication skills: able to explain technical concepts clearly to non-technical audiences.
Interest in education, learning design, or working in academic environments.
Fluent in English; Spanish is a plus.

What we expect from the candidate?

Curiosity, enthusiasm, and a proactive attitude toward learning new technologies and tools.
Strong interpersonal skills and comfort working with faculty, staff, and cross-functional teams.
Ability to work both conceptually (understanding the "why") and practically (delivering working solutions).
Self-motivated with attention to detail and ability to manage multiple tasks.
Comfortable in a fast-paced, innovative environment with evolving priorities.
Willingness to support pilots, live sessions, and faculty-facing activities (occasional flexibility required).

Work environment

The position operates in a dynamic academic environment with interactions across faculty, program teams, and external partners. The role is based in Barcelona, with occasional travel for project implementation in other IESE campuses worldwide.

Reporting Structure

The role is embedded within the Learning Innovation Unit (LIU) and operates within the AI & Learning Technology area. Day-to-day priorities, technical standards, and delivery quality are defined within this area, in coordination with the Executive Director of LIU and relevant Associate Directors depending on the initiative.

The role collaborates closely with a range of internal stakeholders across IESE, including learning design, data, faculty enablement, and program teams.