Machine Learning Engineer
TechJobs.ie Job Insights
At a glance
Technologies & skills
What you'll be doing
Hands-on Machine Learning Engineer (12-month contract, hybrid in Ireland) to design, build, and deploy production-ready AI/ML solutions. Own end-to-end ML initiatives, collaborate with cross-functional teams, and champion MLOps, DataOps, and DevSecOps principles across scalable data and ML platforms.
- 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
- machine learning engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Machine Learning Engineer
12 Month Initial Contract | Hybrid | 2 Days Onsite
We're looking for an experienced Machine Learning 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 role is a hands-on Machine Learning Engineer contract focused on designing, building, and deploying production-ready AI/ML solutions using Python, Snowflake, AWS, and modern MLOps practices in a hybrid setting in Ireland.
- Hands-on ML engineering with production deployment·High
- GenAI, LLMs, and RAG architecture experience·High
- Cloud data platform (Snowflake, AWS) expertise·High
- MLOps and CI/CD automation·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Summarise End-to-End ML Projects
0–8 minSelect 1-2 projects where you owned the full ML lifecycle, and prepare concise summaries highlighting challenges and outcomes.
Deep Dive into GenAI, LLMs, and RAG Deployments
8–15 minPrepare technical details and lessons learned from deploying generative AI or RAG-based solutions.
Refresh Knowledge of Snowflake, AWS, and MLOps Tools
15–21 minReview your experience with Snowflake, AWS, and automation tools, focusing on integration and scalability.
Prepare Examples of Data Governance and Responsible AI
21–26 minRecall specific instances where you addressed data privacy, security, or compliance in ML projects.
Draft Role-Specific Questions to Ask
26–30 minWrite down 3-4 thoughtful questions about the team, projects, and technical environment.
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 (Snowflake, AWS)
Highlight your expertise in building scalable data and ML solutions using Snowflake and cloud infrastructure, which is central to the role.
MLOps, CI/CD, and Automation
Illustrate your understanding and application of modern software engineering, automation, and deployment practices to ensure robust, maintainable ML systems.
Cross-functional Collaboration
Prepare examples of working with data, product, and business teams to translate ambiguous requirements into valuable AI/ML solutions.
What to research
, 4 itemsSnowflake and Cloud Data Platform Integration
Review your experience with Snowflake and how you have integrated it with ML pipelines and cloud services.
Productionising GenAI, LLMs, and RAG Architectures
Prepare detailed examples of deploying generative AI and retrieval-augmented generation solutions in production.
MLOps, CI/CD, and Automation Tools
Refresh your knowledge of MLOps practices, CI/CD pipelines, and automation frameworks relevant to ML deployment.
Data Governance and Responsible AI
Be ready to discuss how you have addressed data privacy, security, and responsible AI in previous roles.
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? Clarifies the technical focus and helps you tailor your examples to their needs.
How is success measured for ML solutions in this team?
Why ask this? Shows your interest in delivering business value and understanding performance metrics.
What is the current state of the MLOps and CI/CD infrastructure?
Why ask this? Helps you gauge the maturity of their engineering practices and where you can contribute.
How does the team collaborate with product and business stakeholders during project delivery?
Why ask this? Assesses cross-functional dynamics and communication expectations.
What are the biggest technical or organisational challenges facing the AI/ML team right now?
Why ask this? Identifies pain points where your skills could add value.
Are there opportunities to contribute to the evolution of the AI/ML platform or data products?
Why ask this? Shows your interest in long-term impact and platform development.
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