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. Responsible for end-to-end ML lifecycle, collaborating with cross-functional teams, and championing MLOps, DataOps, and DevSecOps principles.
- 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
- Architecture / system design
- Indicated in the listing
- 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 environment in Ireland.
- Hands-on ML engineering with production deployment·High
- Cloud data platform expertise (Snowflake, AWS)·High
- MLOps and automation·High
- Generative AI and LLMs·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Summarize End-to-End ML Project Experience
0–8 minSelect 1-2 projects where you owned the full ML lifecycle and prepare concise STAR-format summaries.
Refresh Technical Knowledge on Snowflake, AWS, and Data Pipelines
8–15 minReview documentation and your notes on Snowflake, AWS, and building scalable data pipelines for ML.
Revisit MLOps and CI/CD Implementations
15–21 minList out specific tools, pipelines, and automation strategies you have used for model deployment and monitoring.
Prepare GenAI, LLM, and RAG Production Use Cases
21–26 minIdentify concrete examples of deploying GenAI or LLM solutions, focusing on technical and business outcomes.
Draft Targeted Questions for the Interviewer
26–30 minWrite down 3-4 questions from the provided list to ask during the interview.
Talking points
, 5 itemsEnd-to-End ML Lifecycle Ownership
Be ready to discuss examples where you led projects from experimentation through deployment and ongoing optimisation, as the role emphasizes full lifecycle responsibility.
Productionising ML and GenAI Solutions
Prepare to explain how you have taken ML or GenAI models (including LLMs or RAG) into production, focusing on scalability, reliability, and business impact.
Cloud Data Platforms and Snowflake
Demonstrate your experience building data pipelines and ML solutions using Snowflake and cloud platforms, as this is a core technical requirement.
MLOps, CI/CD, and Automation
Showcase your understanding and practical application of MLOps, CI/CD, and automation for model deployment, monitoring, and maintenance.
Collaboration with Cross-Functional Teams
Highlight situations where you worked with data, product, and business teams to translate ambiguous requirements into valuable AI/ML solutions.
What to research
, 4 itemsReview Recent ML Projects with End-to-End Ownership
Prepare detailed examples where you led ML or GenAI solutions from design to production, including challenges and outcomes.
Deepen Knowledge of Snowflake and Cloud Data Platforms
Refresh your understanding of Snowflake features, data pipeline design, and integration with ML workflows on cloud platforms.
Brush Up on MLOps, CI/CD, and Automation Tools
Review your experience with MLOps practices, CI/CD pipelines, and automation for model deployment and monitoring.
Prepare Examples of Generative AI, LLMs, and RAG in Production
Be ready to discuss hands-on work with LLMs, RAG architectures, and GenAI, focusing on production challenges and solutions.
Questions to ask
, 6 itemsWhat are the main business objectives driving current AI/ML initiatives?
Why ask this? Clarifies how your work will align with business value and priorities.
How is the ML engineering team structured, and how does it collaborate with data, product, and business teams?
Why ask this? Helps you understand team dynamics and cross-functional collaboration.
What are the biggest technical challenges currently faced in deploying and maintaining ML solutions?
Why ask this? Gives insight into the complexity and maturity of the ML stack.
Which tools and frameworks are most commonly used for MLOps, CI/CD, and monitoring in your environment?
Why ask this? Clarifies the technical stack and expectations for automation and deployment.
How does the company approach data governance, privacy, and responsible AI in production systems?
Why ask this? Shows your interest in compliance and ethical AI practices.
What does success look like for this role in the first 6-12 months?
Why ask this? Helps you understand performance expectations and key deliverables.
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