Senior Machine Learning Engineer (Full-Stack / GenAI)

CPL · Recruitment agency
Contract•Full-stack•€550/day (EUR)•Galway, Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Contract
Workplace
Hybrid
Day rate
€550/day (EUR)
Category
Full-stack

Technologies & skills

Cloud & infrastructure

GitHub ActionsKubernetesAWS

Other technical skills

OpenTelemetryLangChainSnowflake

Secondary technologies

PrometheusGrafana

What you'll be doing

Seeking a Senior Machine Learning Engineer (Full-Stack / GenAI) in Galway, Ireland (hybrid). The role focuses on integrating ML/GenAI into scalable applications, building end-to-end systems, and collaborating with cross-functional teams to deliver robust enterprise solutions. 10+ years' experience required.

  • Build full-stack applications integrating ML/GenAI services
  • Convert research prototypes into production APIs and scalable microservices
  • Deliver APIs, UIs, and services for core business applications
  • Create secure, reliable platforms for RAG, model serving, and agentic applications
  • Develop reusable patterns, libraries, and frameworks for ML integration
  • Collaborate with ML researchers, product teams, and cloud engineering

Key requirements

Must-have

  • 10+ years professional software engineering experience
  • 4–6 years production experience with machine learning or GenAI systems
  • Bachelor’s or Master’s in Computer Science, Engineering, or related field
  • Hands-on with RAG pipelines, embeddings, vector search, agentic frameworks (e.g., LangChain)
  • Proficiency with vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus)
  • Strong expertise in AWS (S3, Lambda, ECS/EKS, SageMaker, API Gateway)
  • Advanced Python
  • Experience with modern front-end frameworks (React/Next.js, Angular)

Nice-to-have

  • Experience with model monitoring, evaluation frameworks, and automated alerting
  • Experience with Prometheus, Grafana, OpenTelemetry

Experience: 10+ years professional software engineering; 4–6 years ML/GenAI in production

Role signals

Technical focus
full-stack machine learning engineering, cloud-native systems, ML/GenAI integration
Architecture / system design
Indicated in the listing
Hands-on vs management
Hands-on

Full job description

Senior Machine Learning Engineer (Full-Stack / GenAI)

  • Location: Galway, Ireland (Hybrid options available)
  • Job Type: 12-Month Contract
  • Daily Rate: €550 per day

About the Role

We are seeking a Senior Machine Learning Engineer with exceptional software engineering fundamentals to join our team in Galway. In this role, you will seamlessly integrate Machine Learning and GenAI capabilities into scalable, production-grade applications.

This position is ideal for an experienced engineer (10+ years) who thrives on building end-to-end systems—including front-ends, back-ends, APIs, and ML-powered services—rather than academic research. You will partner closely with ML researchers, data scientists, and platform teams to transform prototypes, RAG pipelines, and agentic workflows into robust enterprise systems.

The Expertise We’re Looking For

  • Education: Bachelor’s or Master’s in Computer Science, Engineering, or a related technical field.

  • Core Experience: 10+ years of professional software engineering experience (full-stack, distributed systems, APIs, cloud).

  • ML/GenAI Focus: 4–6 years of experience working with machine learning or GenAI systems in production.

  • AI Frameworks: Hands-on experience designing and deploying RAG pipelines, embeddings, vector search, and agentic frameworks (e.g., LangChain, CrewAI, LangGraph, AutoGen).

  • Vector Databases: Proficiency with FAISS, Pinecone, Weaviate, or Milvus.

  • Cloud Architecture: Strong expertise in cloud-native engineering, specifically AWS (S3, Lambda, ECS/EKS, SageMaker, API Gateway).

  • Development Stack: Advanced Python paired with modern front-end frameworks (React/Next.js, Angular) and back-end systems (Node.js, Java, C#, or Go).

  • Data Systems: Experience with relational databases (Oracle), cloud data warehouses (Snowflake), and knowledge graphs (Neo4j, RDF/SPARQL).

  • DevOps & Rigour: Proven experience with CI/CD pipelines, Docker, Kubernetes, GitHub Actions, and Infrastructure as Code (IaC).

  • Ethical AI: Strong understanding of Responsible AI, data privacy, and ethical considerations.

  • Bonus: Experience with model monitoring, evaluation frameworks, and automated alerting.
    The Skills You Bring

  • End-to-End Engineering: Ability to build complete full-stack applications that seamlessly integrate ML/GenAI services.

  • Productising ML: Highly skilled at converting research notebooks and prototypes into production APIs and scalable microservices.

  • System Design: Comfortable managing distributed systems, containerisation, scaling, observability, and resilience.

  • Observability Engineering: Experience with Prometheus, Grafana, OpenTelemetry, and logging/metrics pipelines.

  • Collaboration: Strong communication skills to articulate architectural decisions and technical concepts to non-technical business partners.
    The Value You Deliver

  • Build Full-Stack Components: Deliver the APIs, UIs, and services that bring ML and GenAI capabilities directly into core business applications.

  • Scalable Platforms: Create secure, reliable platforms for RAG, model serving, and agentic applications.

  • Drive Productivity: Improve engineering velocity by creating reusable patterns, libraries, and frameworks for ML integration.

  • Cross-Functional Impact: Collaborate closely with ML researchers, product teams, and cloud engineering to deliver measurable enterprise business impact.
    #LI-AF1

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 for a Senior Machine Learning Engineer (Full-Stack / GenAI) who will build and productionize end-to-end ML/GenAI-powered applications, integrating advanced AI capabilities into scalable, cloud-native systems.

  • Full-stack ML/GenAI integration·Medium
  • Cloud-native architecture (AWS, Kubernetes)·Medium
  • Productionizing ML systems·Medium
  • DevOps and observability·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review and Prepare Full-Stack ML Project Examples

    0–8 min

    Select 1-2 relevant projects where you built or productionized ML/GenAI-powered applications, and outline the technical and business impact.

  2. Refresh Knowledge on RAG Pipelines and Vector Databases

    8–14 min

    Study your experience with RAG, embeddings, and vector search, focusing on design, deployment, and optimization.

  3. Summarize AWS and Cloud-Native Engineering Experience

    14–20 min

    Prepare concise talking points on your use of AWS services, Kubernetes, and DevOps tools in ML/GenAI contexts.

  4. Prepare for Responsible AI and Data Privacy Discussions

    20–25 min

    List concrete examples of how you have addressed ethical AI and data privacy in past projects.

  5. Draft Questions for the Interviewer

    25–30 min

    Select and tailor 2-3 questions from the provided list to ask during your interview.

Likely questions

, 8 items

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

Talking points

, 6 items

  • End-to-End Full-Stack Development

    Demonstrate your ability to build applications that span front-end, back-end, and ML/GenAI services, as this is central to the role.

  • Productionizing ML/GenAI Systems

    Showcase experience transforming research prototypes into robust, scalable APIs and microservices, which is a key responsibility.

  • Cloud-Native Engineering (AWS, Kubernetes)

    Highlight your expertise in deploying and managing distributed systems in the cloud, as the role emphasizes AWS and containerization.

  • RAG Pipelines, Embeddings, and Vector Databases

    Prepare examples of designing and deploying retrieval-augmented generation pipelines and working with vector search technologies.

  • DevOps and Observability

    Demonstrate your experience with CI/CD, monitoring, and infrastructure as code, which are essential for delivering reliable ML-powered platforms.

  • Ethical and Responsible AI

    Be ready to discuss how you address data privacy, ethical considerations, and responsible AI practices in production systems.

What to research

, 5 items

  • Review RAG Pipelines and Vector Databases

    Refresh your knowledge and prepare examples of designing, deploying, and optimizing retrieval-augmented generation pipelines and working with vector databases like FAISS, Pinecone, Weaviate, or Milvus.

  • Deep Dive into AWS ML Services

    Be ready to discuss your experience with AWS services such as S3, Lambda, ECS/EKS, SageMaker, and API Gateway, focusing on ML/GenAI deployment scenarios.

  • Showcase Full-Stack ML Application Projects

    Prepare detailed stories of projects where you built or contributed to full-stack applications integrating ML/GenAI, including both front-end (React/Next.js) and back-end (Node.js, Python) components.

  • Prepare for DevOps and Observability Questions

    Review your experience with CI/CD pipelines, Docker, Kubernetes, GitHub Actions, and observability tools like Prometheus, Grafana, and OpenTelemetry.

  • Responsible AI and Data Privacy

    Be ready to discuss concrete steps you have taken to ensure ethical AI, data privacy, and compliance in production systems.

Questions to ask

, 6 items
  1. What are the main business applications or products that will leverage the ML/GenAI capabilities I would help build?

    Why ask this? Clarifies the real-world impact and context of your work.

  2. How mature are your current RAG pipelines and agentic workflows, and what are the biggest technical challenges you face?

    Why ask this? Helps you understand the technical landscape and where your expertise will be most valuable.

  3. What is the typical collaboration process between ML engineers, researchers, and product teams?

    Why ask this? Gives insight into team dynamics and expectations for cross-functional work.

  4. How do you approach model monitoring, evaluation, and automated alerting in production?

    Why ask this? Shows your interest in operational excellence and reliability.

  5. What are the expectations for contributing to reusable frameworks or libraries for ML integration?

    Why ask this? Clarifies your role in shaping engineering best practices and codebase quality.

  6. How does the team ensure responsible AI and data privacy in deployed systems?

    Why ask this? Demonstrates your commitment to ethical AI and aligns your values with the company.

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Apply NowApply before: 12 Nov 2026