Machine Learning Engineer

Reperio Human Capital · Recruitment agency
Full-time•AI•€75k-85k/year (EUR)•Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Full-time
Workplace
Hybrid
Salary
€75k-85k/year (EUR)
Category
AI

Technologies & skills

Primary technologies

Python

Cloud & infrastructure

KubernetesAWSDockerCI/CD

Other technical skills

TensorFlowPyTorchLLMs

What you'll be doing

Join a Dublin-based company as a Machine Learning Engineer, focusing on deploying and maintaining machine learning models in production. You'll turn prototypes into scalable services, build automated pipelines, monitor model performance, and ensure reliability for real customers. Hybrid work in Ireland.

  • Turn prototypes into production services that handle real traffic
  • Build automated pipelines to train, test, and release new model versions
  • Spot model drift and performance problems early
  • Keep models fast to respond and affordable to run

Key requirements

Must-have

  • Roughly 3+ years shipping machine learning to production
  • Confident in Python
  • Hands-on experience with PyTorch, TensorFlow, or scikit-learn
  • Deployed models on AWS or another major cloud
  • Comfortable with Docker and CI/CD
  • Understands how to judge whether a model is good enough to release

Nice-to-have

  • Experience with MLflow
  • Experience with Kubernetes
  • Experience with real-time model serving
  • Experience with LLM or generative AI projects

Experience: 3+ years shipping machine learning to production

Role signals

Technical focus
Machine Learning Engineering, Model Deployment, MLOps
Hands-on vs management
Hands-on

Full job description

Machine Learning Engineer | Dublin (hybrid)

A company in Dublin is putting machine learning at the centre of its products and needs an engineer to make that happen. The data scientists build the models; you'll be the person who makes them fast, reliable and ready for real customers. Expect variety: one week you're deploying a new model, the next you're working out why one has started making worse predictions.

Your focus

  • Turning prototypes into production services that can handle real traffic

  • Building automated pipelines to train, test and release new model versions

  • Spotting model drift and performance problems early, before they reach users

  • Keeping models fast to respond and affordable to run

Your experience

  • A few years (roughly 3+) shipping machine learning to production

  • Confident in Python, with hands-on PyTorch, TensorFlow or scikit-learn

  • Has deployed models on AWS (SageMaker a plus) or another major cloud

  • Comfortable with containers (Docker) and automated release pipelines (CI/CD)

  • Understands how to judge whether a model is good enough to release

Extra credit: MLflow, Kubernetes, real-time model serving, or LLM / generative AI projects.

The package

  • Competitive salary

  • Pension scheme

Apply with your CV below, or contact Ashton Simms at Reperio Human Capital for a confidential chat

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 centers on deploying and maintaining machine learning models in production, ensuring reliability, scalability, and performance. The position emphasizes hands-on engineering, automation, and operational excellence in a hybrid Dublin-based environment.

  • Hands-on ML Engineering·High
  • MLOps and Automation·High
  • Cloud and Containerization·High

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 Deployment Projects

    0–8 min

    Select 1-2 strong examples of taking models to production, focusing on your role, challenges, and results.

  2. Brush Up on CI/CD and Automation Tools

    8–14 min

    Refresh your knowledge of building automated pipelines for ML, including any experience with MLflow, Jenkins, or similar tools.

  3. Revisit AWS and Docker Skills

    14–21 min

    Prepare to discuss specific AWS services (especially SageMaker) and Docker workflows you have used for ML deployment.

  4. Prepare for Monitoring and Optimization Questions

    21–27 min

    Think through examples where you detected and resolved model drift or performance issues, and how you optimized for speed and cost.

  5. Draft Role-Specific Questions

    27–30 min

    Write down 2-3 questions about the team's workflow, tech stack, and challenges to ask during the interview.

Likely questions

, 7 items

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

Talking points

, 5 items

  • End-to-End Model Deployment

    Be ready to discuss how you have taken ML models from prototype to production, highlighting challenges and solutions.

  • Automated Training and Release Pipelines

    Demonstrate experience building robust, automated workflows for model retraining, testing, and deployment.

  • Cloud Infrastructure and Containerization

    Showcase your ability to deploy and manage models using AWS, Docker, and optionally Kubernetes, as these are core to the stack.

  • Model Monitoring and Drift Detection

    Prepare examples of how you have identified and addressed model performance issues or drift in production.

  • Performance Optimization and Cost Management

    Explain strategies you have used to keep models responsive and cost-effective in real-world settings.

What to research

, 4 items

  • Recent ML Production Projects

    Review your experience deploying ML models, focusing on challenges and outcomes.

  • CI/CD and Automated ML Pipelines

    Refresh your knowledge of building and maintaining automated pipelines for ML workflows.

  • AWS Model Deployment

    Be ready to discuss your hands-on experience with AWS services for ML, especially SageMaker, ECS, and ECR.

  • Model Monitoring and Drift Detection

    Prepare examples of how you have monitored models in production and addressed drift or performance issues.

Questions to ask

, 6 items
  1. What is the typical workflow for taking a model from prototype to production here?

    Why ask this? Clarifies the team's process and your role in it.

  2. Which AWS services and tools are most commonly used for model deployment and monitoring?

    Why ask this? Helps you understand the technical environment and expectations.

  3. How does the team handle model drift and performance degradation in production?

    Why ask this? Reveals the maturity of monitoring and response processes.

  4. What are the biggest challenges the team has faced with scaling ML services to real users?

    Why ask this? Gives insight into current pain points and where you can add value.

  5. Is there an opportunity to work on LLMs or generative AI projects in the near future?

    Why ask this? Assesses potential for growth and alignment with your interests.

  6. How is success measured for this role and for deployed models?

    Why ask this? Clarifies expectations and performance metrics.

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Apply NowApply before: 29 Nov 2026