AI / ML Engineer
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At a glance
Technologies & skills
What you'll be doing
Hands-on AI/ML Engineer (12-month contract, hybrid in Ireland) to design, build, and deploy production-ready machine learning and AI solutions. Work end-to-end on solution design, experimentation, deployment, and optimisation, collaborating with cross-functional teams to deliver scalable, secure, and valuable AI/ML products.
- Design, develop, test, and deploy AI/ML models and intelligent applications
- Build and productionise machine learning and GenAI solutions, including RAG-based architectures
- Work with Snowflake and modern cloud data platforms to develop scalable data and ML solutions
- Develop robust software using modern software engineering, testing, automation, and CI/CD practices
- Work across the full ML lifecycle, from experimentation and prototyping through to deployment, monitoring, and optimisation
- Build and enhance data pipelines and solutions that support AI/ML workloads and analytics
- Contribute to the evolution of AI/ML platforms, data products, and engineering capabilities
- Improve the performance, scalability, reliability, and quality of data and ML pipelines
Key requirements
Must-have
- Strong hands-on software engineering experience with Python
- Proven experience building and deploying machine learning and AI solutions in production
- Strong experience working with Snowflake, cloud data platforms, and modern data architectures
- Experience working across Linux, AWS, containers, and data science / ML platforms
- Practical experience with Generative AI, LLMs, and RAG architectures
- Understanding of MLOps, CI/CD, model deployment, monitoring, and automation
- A strong software engineering mindset, with a focus on scalability, maintainability, testing, and reliability
- Ability to take ownership of technical solutions and work effectively across engineering, data, product, and business teams
Nice-to-have
- Experience with R
Role signals
- Technical focus
- AI/ML engineering, end-to-end solution ownership, production deployment
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
AI / ML Engineer
12 Month Initial Contract | Hybrid | 2 Days Onsite
We're looking for an experienced AI / ML Engineer to design, build, and deploy production-ready machine learning and AI solutions across data science, analytics, and intelligent applications.
This is a hands-on, high-impact engineering role for someone who can take ownership of AI/ML initiatives end-to-end from solution design and experimentation through to deployment, optimisation, and ongoing improvement.
You'll work closely with data, technology, product, and business teams to translate complex data and AI challenges into scalable, secure, and commercially valuable solutions.
What You'll Be Doing
- Design, develop, test, and deploy AI/ML models and intelligent applications
- Build and productionise machine learning and GenAI solutions, including RAG-based architectures
- Work with Snowflake and modern cloud data platforms to develop scalable data and ML solutions
- Develop robust software using modern software engineering, testing, automation, and CI/CD practices
- Work across the full ML lifecycle, from experimentation and prototyping through to deployment, monitoring, and optimisation
- Build and enhance data pipelines and solutions that support AI/ML workloads and analytics
- Contribute to the evolution of AI/ML platforms, data products, and engineering capabilities
- Improve the performance, scalability, reliability, and quality of data and ML pipelines
- Champion MLOps, DataOps, and DevSecOps principles across development and delivery
- Apply strong practices around data governance, privacy, security, and responsible AI
- Collaborate with cross-functional teams to identify opportunities where AI and machine learning can deliver measurable business value
What You'll Bring
- Strong hands-on software engineering experience with Python, with additional experience in R
- Proven experience building and deploying machine learning and AI solutions in production
- Strong experience working with Snowflake, cloud data platforms, and modern data architectures
- Experience working across Linux, AWS, containers, and data science / ML platforms
- Practical experience with Generative AI, LLMs, and RAG architectures
- Understanding of MLOps, CI/CD, model deployment, monitoring, and automation
- A strong software engineering mindset, with a focus on scalability, maintainability, testing, and reliability
- Ability to take ownership of technical solutions and work effectively across engineering, data, product, and business teams
- Strong problem-solving skills and the ability to turn ambiguous business problems into practical AI/ML solutions
**
Please note:** Applicants must have unrestricted, full-time eligibility to work in Ireland.
For more information or to apply in confidence, please contact Scott Hool.
Reperio Human Capital acts as an Employment Agency and an Employment Business.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the AI/ML Engineer role is a hands-on position focused on designing, building, and deploying production-ready machine learning and AI solutions, with a strong emphasis on end-to-end ownership, modern data platforms, and scalable engineering practices.
- Hands-on AI/ML engineering·Medium
- Production deployment of GenAI/LLMs·High
- Cloud data platform integration·High
- MLOps and automation·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review End-to-End AI/ML Project Examples
0–8 minSelect 1-2 projects where you owned the full ML lifecycle, and prepare concise stories highlighting design, deployment, and optimisation.
Refresh Knowledge of GenAI, LLMs, and RAG Deployments
8–15 minSummarise your hands-on experience with generative AI and RAG architectures, focusing on production challenges and solutions.
Revisit Snowflake and Cloud Data Platform Integrations
15–20 minList specific ways you have used Snowflake and AWS in ML workflows, including any automation or orchestration tools.
Prepare for MLOps and CI/CD Discussions
20–25 minOutline your experience with CI/CD, containerisation, and monitoring tools for ML, ready to discuss technical details.
Plan Questions and Research Team Practices
25–30 minDraft your top questions for the interview and research the company's approach to AI/ML engineering and cross-team collaboration.
Talking points
, 5 itemsEnd-to-End ML Lifecycle Ownership
Demonstrate your ability to take projects from experimentation through deployment, monitoring, and optimisation, showing initiative and technical breadth.
Productionising GenAI and RAG Architectures
Showcase practical experience with generative AI, large language models, and retrieval-augmented generation, especially in real-world, scalable deployments.
Cloud Data Platforms and Snowflake Expertise
Highlight your experience building scalable data and ML solutions using Snowflake and cloud platforms, as this is central to the role.
MLOps, CI/CD, and Automation
Illustrate your understanding and application of modern software engineering, automation, and operational best practices for reliable ML delivery.
Collaboration with Cross-Functional Teams
Prepare examples of working with data, product, and business teams to translate ambiguous problems into valuable AI/ML solutions.
What to research
, 4 itemsSnowflake and Cloud Data Platform Integration
Review your experience with Snowflake and how you have used it in ML workflows, including data ingestion, transformation, and integration with AWS.
Productionising GenAI, LLMs, and RAG Architectures
Prepare concrete examples of deploying generative AI models and RAG systems, focusing on scalability, reliability, and monitoring.
MLOps, CI/CD, and Automation Tools
Refresh your knowledge of CI/CD pipelines, containerisation, and automation tools relevant to ML deployment and monitoring.
Responsible AI, Data Governance, and Security
Be ready to discuss how you address data privacy, governance, and security in AI/ML projects, including any frameworks or best practices you follow.
Questions to ask
, 6 itemsWhat are the main types of AI/ML projects currently in production or planned for the next year?
Why ask this? To understand the practical focus and technical challenges you will face.
How is success measured for AI/ML initiatives within the team?
Why ask this? To clarify expectations and how your impact will be evaluated.
What is the current state of the MLOps and CI/CD infrastructure?
Why ask this? To assess the maturity of engineering practices and where you can contribute.
How do cross-functional teams (data, product, business) typically collaborate on AI/ML projects?
Why ask this? To gauge the level of collaboration and communication required.
What are the biggest challenges the team faces in deploying and maintaining AI/ML solutions at scale?
Why ask this? To identify pain points and areas where your skills can add value.
Are there opportunities to contribute to the evolution of AI/ML platforms or data products beyond project delivery?
Why ask this? To explore potential for broader technical influence and growth.
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