Software Engineer, AI Data - Galway, Ireland

EducationPals; Galway; Hybrid; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

EducationPals builds high-quality datasets at petabyte scale by combining engineering, infrastructure, and research. We are seeking a strong software engineer who enjoys solving complex problems, exploring new data sources, and creating systems that make AI training more efficient and scalable.

You’ll discover new sources of audio data and build reliable pipelines to bring them into our training datasets, while building and improving large-scale data-processing systems optimized for data quality

Qualifikationen

• Strong software engineering foundation.
• Experience scaling data-processing pipelines.
• Familiarity with AI-related data workflows.
• Ability to design reliable data ingestion and validation processes.

Aufgaben

• Discover new sources of audio data and build pipelines to ingest them into training datasets.
• Develop and optimize large-scale data-processing systems for quality and performance.

Kenntnisse

AI fundamentals

Python

Cloud

User research

Jobbeschreibung

We build high-quality datasets at petabyte scale and low cost by combining engineering, infrastructure, and research. We're looking for a strong software engineer who enjoys solving complex problems, exploring new data sources, and building systems that make AI training more efficient and scalable. What You’ll Do: Discover new sources of audio data and build reliable pipelines to bring them into our training datasets. Build and improve large-scale data-processing systems, optimizing for data qua

Relevance is based on skills and concepts, not a guarantee that you qualify.

Covered skills: AI fundamentals Python Cloud User research

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

1. Context Architecture for Generative AI

Design systems that supply business rules, knowledge, and constraints to models at runtime.

2. Building Retrieval Augmented Generation Pipelines

Implement search and retrieval systems that ground model outputs in curated, current information.

3. Permission and Access Control Design

Define what AI systems can see, modify, and execute based on user roles and business rules.

4. Review Loops and Approval Workflows

Architect human and automated checkpoints that catch errors before outputs reach customers or systems.

5. Observability and Audit Systems

Track AI decisions, actions, and outcomes to enable debugging, compliance, and trust.

6. Standards and Quality Gates

Encode brand guidelines, compliance rules, and success criteria into testable system requirements.

7. Production Deployment Patterns

Design scalable, reliable systems that integrate generative AI into existing business operations.