Senior Machine Learning Engineer (Full-Stack / GenAI)

Cpl Group; Galway; Hybrid; EUR 101,000 - 111,000; Full time

Job summary

Cpl Group in Galway, Ireland is seeking a Senior Machine Learning Engineer with end-to-end software engineering skills to join our hybrid team. You will integrate ML and GenAI capabilities into production-grade applications, spanning front-end, back-end, APIs, and ML-powered services.

The role requires 10+ years of software experience, 4–6 years in ML/GenAI production, and strong Cloud (AWS) and CI/CD expertise.

Qualifications

• 10+ years of professional software engineering experience (full-stack, distributed systems, APIs, cloud).
• 4–6 years of production ML/GenAI experience.
• Hands‑on experience designing and deploying RAG pipelines, embeddings, vector search, and agentic frameworks.
• Proficiency with AWS (S3, Lambda, ECS/EKS, SageMaker, API Gateway).

Responsibilities

• Build end-to-end full‑stack applications that integrate ML/GenAI services.
• Collaborate with ML researchers, data scientists, and platform teams to productionize prototypes and pipelines.
• Design and maintain scalable, enterprise-grade systems with robust APIs and UIs.
• Implement CI/CD, Docker, Kubernetes, and IaC for reliable deployments.
• Focus on Responsible AI, data privacy and ethics.

Skills

End-to-End Engineering

Productising ML

System Design

Observability Engineering

Collaboration

Education

Bachelor’s or Master’s in CS/Engineering

Tools

LangChain

CrewAI

LangGraph

AutoGen

FAISS

Pinecone

Weaviate

Milvus

AWS (S3, Lambda, ECS/EKS, SageMaker, API Gateway)

React/Next.js

Angular

Node.js

Java

Go

Oracle

Snowflake

Neo4j

RDF/SPARQL

Docker

Kubernetes

GitHub Actions

IaC

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