TechJobs.ie Job Insights
At a glance
- Employment type
- Full-time
- Location
- Ireland
- Workplace
- On-site
- Category
- Data engineering
Technologies & skills
Other technical skills
What you'll be doing
Foundational AI Data Engineer role to design and build scalable, production-grade data infrastructure for AI-powered products. Shape AI strategy, tooling, and architecture in a greenfield, cloud-native environment. High autonomy, technical leadership, and direct impact on company innovation.
- Develop high-performance data pipelines and platforms for AI/ML and agentic systems
- Ensure data quality, observability, and governance across pipelines
- Promote fairness, transparency, and security in model development and deployment
- Collaborate with cross-functional teams to deliver AI-driven features and tools
- Define and standardize best practices for AI deployment in a cloud-native environment
Key requirements
Must-have
- Background in designing and delivering AI/ML-ready data infrastructure in production
- Experience with cloud-native data tools on Azure, AWS, or GCP
- Familiarity with agentic architectures and orchestration tools
- Understanding of ETL/ELT pipelines, feature stores, and real-time or batch data processing
- Awareness of AI/ML model performance metrics, A/B testing, and bias/fairness assessment
Role signals
- Technical focus
- data engineering, AI infrastructure, cloud-native systems
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Senior AI Data Engineer
(First AI-Focused Hire | High Impact Role)
Join us at the ground floor of something transformative.
We’re seeking a Senior AI Data Engineer to support the development of our next generation of AI-powered products. This is a foundational hire offering the rare opportunity to shape the direction of our AI strategy, tooling, and architecture.
As a senior technical leader, you’ll design and build scalable, production-grade data infrastructurethat powers intelligent, agentic systems. Your work will directly impact the way we deliver intelligent features and automate complex workflows for our customers.
We value autonomy, initiative, and technical excellence. You’ll thrive here if you’re excited by the idea of working with modern AI architectures, experimenting with the best tools for the job (whether on Azure, AWS, or GCP), and helping define our technical roadmap for the years ahead.
What You’ll Do
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Develop high-performance data pipelines and platforms to support production-grade AI/ML and agentic systems.
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Ensure data quality, observability, and governance across all stages of the pipeline.
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Help shape practices that promote fairness, transparency, and security in model development and deployment.
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Collaborate with cross-functional teams to deliver AI-driven product features and internal tools.
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Help define and standardize best practices and patterns for AI deployment in a cloud-native environment.
What You Bring
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A strong background in designing and delivering AI/ML-ready data infrastructure in production environments.
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Proven experience with cloud-native data tools—whether on Azure (e.g. Fabric, Data Factory, Synapse, Data Lake, Databricks, Spark), or equivalent stacks on AWS or GCP.
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Familiarity with agentic architectures and orchestration tools like Azure AI Foundry, Semantic Kernel, MCP, Manager Agents, or similar frameworks.
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Solid understanding of ETL/ELT pipelines, feature stores, and real-time or batch data processing.
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Awareness of AI/ML model performance metrics, A/B testing frameworks, and bias/fairness assessment methodologies.
Why This Role Is Unique
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Greenfield AI initiatives – you’ll be leading the way in establishing our AI capability.
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Tooling freedom – we’re cloud-native and open to the best stack for the problem, not tied to a single ecosystem.
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Autonomous role with executive visibility – your contributions will directly shape the company’s innovation strategy.
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Engineering-led culture – collaborative, Agile, feedback-driven, with a focus on doing things right.
Apply Now
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
You will be assessed on your ability to design and deliver production-grade, cloud-native data infrastructure for AI/ML systems. Expect focus on building scalable pipelines, cloud platform expertise (Databricks, Spark, AWS, Azure, GCP), and shaping AI deployment best practices in a greenfield environment.
- AI/ML data infrastructure·High
- Cloud-native tools (AWS/Azure/GCP)·High
- Production-grade pipelines·High
- Agentic architectures·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Cloud-Native Data Engineering Projects
0–10 minIdentify 2-3 relevant projects where you built or scaled AI/ML data pipelines using Databricks, Spark, or cloud platforms.
Summarize Data Quality and Governance Practices
10–17 minPrepare concise examples of how you ensured data quality, observability, and compliance in production environments.
Refresh on Agentic Architectures and Orchestration
17–25 minReview your experience or research key concepts and tools (e.g., Semantic Kernel, Azure AI Foundry) relevant to agentic systems.
Draft Questions for the Interviewer
25–30 minList thoughtful questions about AI strategy, technical stack, and team culture to ask during the interview.
Talking points
, 6 itemsDesigning Scalable Data Pipelines
You will build high-performance data pipelines to support AI/ML and agentic systems; expect questions on architecture, scalability, and reliability.
Cloud-Native Data Tools (Databricks, Spark, AWS, Azure, GCP)
The role requires hands-on experience with modern cloud data stacks; you must demonstrate depth in at least one major cloud and familiarity with others.
Agentic Architectures and Orchestration
Familiarity with agentic systems and orchestration tools is required; you may be asked to explain or design such systems.
Data Quality, Observability, and Governance
Ensuring data quality and governance is a core responsibility; you should be ready to discuss monitoring, lineage, and compliance.
AI/ML Model Deployment Best Practices
You will help define and standardize AI deployment; expect to discuss CI/CD, reproducibility, and cloud-native deployment patterns.
Bias, Fairness, and Transparency in AI
The company values fairness and transparency in model development; be prepared to discuss bias detection and mitigation.
What to research
, 3 itemsCloud-Native AI/ML Data Infrastructure
Expect deep questions on designing and delivering production-grade pipelines using Databricks, Spark, and cloud platforms. Be ready to discuss architecture, tool selection, and deployment strategies.
Defining Best Practices for AI Deployment
You will help set standards for AI deployment in a greenfield environment. Prepare to discuss how you have established or improved best practices, especially in cloud-native contexts.
Ensuring Fairness and Transparency in AI
The role emphasizes promoting fairness, transparency, and security in model development. Be ready to explain your approach to bias detection, fairness assessment, and responsible AI.
Questions to ask
, 6 itemsWhat are the immediate priorities for the AI data engineering function in the first six months?
Why ask this? Clarifies expectations and helps you understand where to focus your efforts early on.
How does the team currently collaborate across engineering, product, and data science?
Why ask this? Reveals the level of cross-functional interaction and support.
Which cloud platforms and tools are most commonly used today, and how open is the team to adopting new ones?
Why ask this? Helps you gauge the flexibility and maturity of the technical stack.
What processes are in place to ensure fairness, transparency, and security in AI development?
Why ask this? Shows the company’s commitment to responsible AI and your potential influence.
How is success measured for this role and for AI-driven product features?
Why ask this? Clarifies performance expectations and alignment with business goals.
What opportunities exist for technical leadership and mentoring as the AI team grows?
Why ask this? Indicates potential for career growth and influence.
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