Senior Software Engineer - Red Hat Data & AI (Cork, IE)
Red HatCorkPosted 28 April 2026
Tech Stack
Job Description
About the job
Red Hat Data & AI is looking for a Principal Software Engineer to join our Domain Engineering team in Cork, Ireland. In this role, you will elevate Red Hat’s data-driven culture and execution by developing data products and AI solutions that support Red Hat’s operational and analytical needs. You will deliver data & AI products (code data) that service the entire organization, not just your own departmental requirements, through an InnerSource collaboration model. Your expertise in software and data engineering practices, combined with a passion for internal customer adoption, will ensure that your data products and AI solutions lead to impactful business outcomes.
What you will do?
Architect and implement AI-enabled capabilities, including LLM integrations, intelligent automation, and AI-assisted workflows.
Collaborate with data engineers, platform engineers, and product teams to integrate AI-powered functionality into enterprise systems.
Ensure reliability, scalability, performance, and observability through strong engineering and operational practices.
Provide technical leadership and mentorship to engineers and guide complex design decisions.
Develop source data pipelines using an ELT (versus ETL) model of data ingestion
Document data product metadata and AI readiness guidance to support Machine Learning and AI/agentic use cases.
Tag and classify your data assets to ensure they are used responsibly throughout the organization, applying masking or restricting access where appropriate
Apply software engineering best practices to your code release process to support CI/CD and a high velocity collaboration model based on InnerSource
Register and maintain the catalog entries for your data products to support discoverability and reuse
Develop automated and resilient processes that support the end-end delivery of business value
Responsibly share your data products with other internal consumers while balancing the core needs of security and compliance
Contribute feedback and recommendations to the Data Platform team in order to remove friction and increase scale for all users
Lead the transition from script-based prototypes (e.g., Streamlit/Gradio) to high-performance, custom web applications
What you will bring?
Exceptional software and data engineering skills that lead to elegant and maintainable data products
Expert level proficiency in using SQL for data transformation
Proficiency in at least one general purpose programming language, eg. Python, Go, Java, Rust, etc.
Practical experience developing, deploying, or integrating Artificial Intelligence (AI) and Machine Learning (ML) models into production
Strong opinions and perspectives that you kindly debate, defend, or change to ensure that the entire team moves as one
Sets and resets the bar on all things quality, from code through to data, and everything in between
Deep empathy for your users of your data products, leading to a constant focus on removing friction, increasing adoption, and delivering business results
Prunes and prioritizes work in order to maximize your contributions and impact
Bias for action and leading by example
Past experience in building enterprise data products that have a high level of governance and compliance requirements
The following experience is considered a plus
Familiarity with open source or inner source development and processes
Familiarity of data mesh architectural principles
Experience with Snowflake, Fivetran, dbt, Airflow / Astronomer
Experience with React (frontend) and Node.js/Fastify (backend)
Familiarity with Generative AI (GenAI) principles and experience working with large language models (LLMs) or related natural language processing technologies.
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About Red Hat
Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40 ... (truncated, view full listing at source)
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