Principal Data Scientist
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid
- Salary
- €130k-140k/year (EUR)
- Category
- Data engineering
What you'll be doing
Principal Data Scientist role in Dublin, acting as the senior technical authority within a growing AI function. Deeply hands-on, influencing architecture and best practices, mentoring senior staff, and partnering with engineering and product leaders to shape the AI roadmap for a globally established enterprise software company.
- Architect and implement large-scale AI/ML systems (NLP, GenAI, RAG, agentic frameworks)
- Lead complex initiatives from research through experimentation to production deployment
- Design scalable training, evaluation, and monitoring pipelines
- Define standards around reproducibility, governance, observability, and responsible AI
- Mentor senior data scientists and ML engineers
- Partner with engineering and product leaders to shape roadmap decisions
Key requirements
Must-have
- 9+ years building ML/AI systems in production
- Experience leading complex AI initiatives end-to-end
- Strong engineering background (distributed systems, scalable ML infrastructure, MLOps)
- Exposure to LLM optimisation, prompting strategies, RAG or agent orchestration
Experience: 9+ years
Role signals
- Technical focus
- AI/ML systems architecture, implementation, and mentoring
- Leadership
- Mentoring
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
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Full job description
The Role
This is a true principal-level position.
You’ll operate as the senior technical authority within a growing AI function in Dublin, deeply hands-on while influencing architecture and best practice across the wider platform.
You’ll
-
Architect and implement large-scale AI/ML systems (NLP, GenAI, RAG, agentic frameworks)
-
Lead complex initiatives from research → experimentation → production deploym
-
Design scalable training, evaluation and monitoring pipelines
-
Define standards around reproducibility, governance, observability and responsible AI
-
Mentor senior data scientists and ML engineers
-
Partner with engineering and product leaders to shape roadmap decisions
This role suits someone who wants both technical depth and platform-level influence.
What You’ll Likely Bring To The Party
-
9 + years building ML/AI systems in production
-
Experience leading complex AI initiatives end-to-end
-
Strong engineering background (distributed systems, scalable ML infrastructure, MLOps)
-
Exposure to LLM optimisation, prompting strategies, RAG or agent orchestration
About the Company
This is a globally established enterprise software company operating at serious scale across international markets. Their platform is commercially mature, widely adopted by large organisations, and continues to expand globally.
The Dublin team forms part of a strategic AI investment area within the business. Rather than building in isolation, the AI group works directly on production systems embedded into a live product used at scale.
It’s the combination of enterprise stability and genuine AI ambition, well-funded, technically strong, and still evolving.
Interested? Hit apply or reach out to me directly at****lee@nineDots.io
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
You will be the senior technical authority for a growing AI function, architecting and implementing large-scale AI/ML systems, leading complex initiatives from research to production, and mentoring senior data scientists and ML engineers. Expect deep hands-on work and platform-level influence.
- AI/ML architecture·High
- Production ML systems·High
- Mentoring & leadership·High
- Scalable pipelines·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Large-Scale AI/ML System Architectures
0–10 minOutline your most complex AI/ML project, focusing on architecture, scalability, and production deployment.
Summarise LLM and GenAI Experience
10–15 minList your hands-on work with LLMs, RAG, and agentic frameworks, noting optimisation and deployment strategies.
Reflect on Mentoring and Leadership Examples
15–25 minRecall specific instances where you mentored senior staff or led cross-functional teams.
Prepare Questions for the Interviewer
25–30 minSelect 2-3 questions about the AI function, technical challenges, and team structure.
Talking points
, 6 itemsArchitecting Large-Scale AI/ML Systems
You will design and implement complex AI/ML solutions, so understanding scalable architectures is central to the role.
End-to-End ML Initiative Leadership
The job requires leading projects from research through experimentation to production deployment.
LLM Optimisation and GenAI Techniques
Experience with LLMs, prompting, RAG, and agentic frameworks is specifically called out.
MLOps and Scalable Infrastructure
A strong engineering background in distributed systems and scalable ML infrastructure is required.
Mentoring Senior Data Scientists and ML Engineers
You will be expected to mentor and guide other senior technical staff.
Responsible AI, Governance, and Observability
Defining standards around reproducibility, governance, and responsible AI is a key responsibility.
What to research
, 4 itemsAI/ML System Architecture and Production Deployment
Expect deep technical questions about designing, scaling, and deploying AI/ML systems in production environments.
LLM Optimisation and Advanced GenAI Techniques
You will likely be tested on your practical experience with LLMs, RAG, and agentic frameworks, as these are highlighted requirements.
Mentoring Senior Technical Staff
The role emphasises mentoring and guiding other senior data scientists and ML engineers, so be ready to discuss your approach and impact.
Responsible AI and Governance Standards
Defining and implementing standards for reproducibility, governance, and responsible AI is a core part of the job.
Questions to ask
, 6 itemsHow is the AI function structured within the Dublin team and across the wider company?
Why ask this? Clarifies your position, influence, and collaboration opportunities.
What are the biggest technical challenges currently facing the AI platform?
Why ask this? Reveals where your expertise will be most valuable and what to expect.
How does the team approach responsible AI and governance in production systems?
Why ask this? Shows the maturity of their processes and your alignment with their standards.
What opportunities exist for mentoring and developing other senior technical staff?
Why ask this? Helps you understand the scope and expectations for leadership and mentoring.
How are roadmap decisions made between AI, engineering, and product teams?
Why ask this? Clarifies your influence on strategic direction and cross-team collaboration.
What does success look like for this role in the first 12 months?
Why ask this? Sets clear expectations and helps you gauge fit.
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