Machine Learning Engineer

Reperio Human Capital · Recruitment agency
Contract•AI•€475-500/day (EUR)•Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Contract
Workplace
Hybrid · 2 days in office
Day rate
€475-500/day (EUR)
Category
AI

Technologies & skills

Primary technologies

PythonR

Cloud & infrastructure

AWSLinux

What you'll be doing

Seeking an experienced Machine Learning Engineer for a 6-12 month hybrid contract in Ireland. Lead the development of AI/ML solutions, data science applications, and analytics platforms. Hands-on role involving end-to-end project ownership, collaboration with cross-functional teams, and driving data-led product strategy.

  • Design, develop, and deploy AI/ML models and data science solutions
  • Build scalable, robust software using modern testing, automation, and engineering practices
  • Help shape data-led product strategy and drive platform improvements
  • Improve data quality, performance, scalability, and reliability
  • Champion DataOps and DevSecOps principles across development and delivery
  • Embed strong practices around data governance, privacy, and security

Key requirements

Must-have

  • Strong hands-on experience with Python and R
  • Experience working across Linux, AWS, containers, and data science platforms
  • Practical knowledge of AI/GenAI development, including RAG architectures
  • Strong software engineering mindset
  • Ability to own solutions and work effectively across teams

Role signals

Technical focus
machine learning, data science, AI/GenAI development
Architecture / system design
Indicated in the listing
Hands-on vs management
Hands-on

Full job description

Machine Learning Engineer

6 - 12 Month Initial Contract | Hybrid | 2 Days Onsite

We're looking for an experienced Machine Learning Engineer to lead the development of machine learning solutions, data science applications, and AI-driven analytics platforms.

This is a hands-on, high-impact role for someone who can take ownership of projects from concept through to delivery, while working closely with cross-functional teams to turn data and AI into real business value.

What You'll Be Doing

  • Design, develop, and deploy AI/ML models and data science solutions
  • Build scalable, robust software using modern testing, automation, and engineering practices
  • Help shape data-led product strategy and drive platform improvements
  • Improve data quality, performance, scalability, and reliability
  • Champion DataOps and DevSecOps principles across development and delivery
  • Embed strong practices around data governance, privacy, and security

What You'll Bring

  • Strong hands-on experience with Python and R
  • Experience working across Linux, AWS, containers, and data science platforms
  • Practical knowledge of AI/GenAI development, including RAG architectures
  • A strong software engineering mindset, with the ability to own solutions and work effectively across teams
    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, developing, and deploying AI/ML solutions, with a strong emphasis on Python, R, AWS, Linux, and modern engineering practices in a hybrid setting in Ireland.

  • Hands-on ML engineering·High
  • Cloud and platform deployment·High
  • DataOps and governance·Medium

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review Recent ML Projects

    0–8 min

    Select 1-2 end-to-end ML projects to discuss, focusing on your role, challenges, and outcomes.

  2. Refresh Python, R, AWS, and Linux Skills

    8–15 min

    Revisit code samples and deployment scripts relevant to the listed stack.

  3. Study GenAI and RAG Architectures

    15–21 min

    Read up on Retrieval-Augmented Generation and prepare to explain or diagram its workflow.

  4. Prepare DataOps and Governance Examples

    21–26 min

    List concrete examples of how you have implemented DataOps, DevSecOps, or data governance in past roles.

  5. Draft Role-Specific Questions

    26–30 min

    Write down 3-4 thoughtful questions to ask the interviewer about the team, projects, and expectations.

Likely questions

, 6 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 5 items

  • Designing and Deploying AI/ML Models

    You will need to demonstrate your ability to take a machine learning project from concept to production, including model selection, training, evaluation, and deployment.

  • Python and R for Data Science

    Hands-on experience with both languages is required; be ready to discuss projects where you used them for data analysis, modeling, or automation.

  • AWS and Linux Ecosystem

    The role expects practical experience with cloud infrastructure and Linux environments; prepare examples of deploying or managing ML workloads in these contexts.

  • AI/GenAI and RAG Architectures

    Practical knowledge of generative AI and Retrieval-Augmented Generation (RAG) is highlighted; be prepared to discuss relevant projects or your understanding of these architectures.

  • DataOps, DevSecOps, and Data Governance

    You will be expected to champion best practices in data operations, security, and governance; prepare to discuss how you have embedded these principles in previous work.

What to research

, 4 items

  • Python and R for ML

    Review your recent projects using Python and R, focusing on data processing, modeling, and automation.

  • AWS and Linux Deployment

    Prepare examples of deploying ML models or data pipelines on AWS and managing workloads in Linux environments.

  • GenAI and RAG Architectures

    Refresh your understanding of generative AI and Retrieval-Augmented Generation, including practical implementation details.

  • DataOps, DevSecOps, and Data Governance

    Be ready to discuss how you have embedded operational, security, and governance best practices in ML workflows.

Questions to ask

, 6 items
  1. What types of AI/ML projects are currently prioritized by the team?

    Why ask this? Clarifies the business context and technical focus of your potential work.

  2. How is success measured for ML solutions in this organization?

    Why ask this? Helps you understand performance expectations and impact.

  3. What is the current tech stack for data science and ML, and are there plans for future changes?

    Why ask this? Gives insight into the tools and platforms you will use and potential learning opportunities.

  4. How does the team approach data governance, privacy, and security for AI/ML projects?

    Why ask this? Shows your interest in compliance and best practices, and helps you gauge organizational maturity.

  5. What opportunities exist for influencing product strategy or platform improvements?

    Why ask this? Assesses your ability to contribute beyond technical delivery.

  6. How are cross-functional teams structured and how do they collaborate on ML initiatives?

    Why ask this? Clarifies collaboration expectations and team dynamics.

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Apply NowApply before: 26 Oct 2026