Senior Machine Learning Engineer

Cpl; Galway; On-site; EUR 120,000 - 180,000; Full time

Job summary

CPL is seeking a Senior Machine Learning Engineer in Ireland’s Galway region to blend ML/GenAI into scalable, production-grade apps. You’ll own end-to-end systems from frontend to backend and ML services, partnering with researchers, data scientists, and platform teams to ship robust, enterprise-grade solutions.

With 10+ years in software, you will design RAG pipelines, deploy vector search, and drive cloud-native engineering across AWS, React-based frontends, and Python backends.

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, or related technical field.
• 10+ years of professional software engineering experience (full stack, distributed systems, APIs, cloud).
• 4–6 years working with machine learning or GenAI systems in production environments.
• Experience designing and deploying RAG pipelines with embeddings and vector search.
• Hands-on with vector databases: FAISS, Pinecone, Weaviate, Milvus, etc.
• Experience with agentic frameworks such as LangChain, CrewAI, LangGraph, AutoGen.
• Strong expertise in cloud-native engineering (AWS preferred).
• Proficient in modern front-end frameworks (React/Next.js, Angular, or similar) and in building robust web applications.
• Back-end development using Python (advanced) and one or more of: Node.js, Java, C#, or Go.
• Familiarity with traditional ML models, their applications, and how to integrate them into products.
• Cloud Platforms: AWS services such as S3, Lambda, ECS/EKS, SageMaker, API Gateway.
• Databases: Experience with relational systems (Oracle), cloud data warehouses (Snowflake), vector DBs, and knowledge graphs (Neo4j, RDF/SPARQL).
• DevOps: CI/CD pipelines, Docker, Kubernetes, GitHub Actions, infrastructure-as-code.
• Understanding of Responsible AI, data privacy, and ethical considerations in ML-driven systems.
• Good to have: Experience with model monitoring, evaluation frameworks, and automated alerting.
• Ability to communicate architectural decisions and technical concepts to non-technical partners.

Responsibilities

• End-to-end engineering: You build full-stack applications that integrate ML/GenAI services seamlessly.
• Productizing ML: Convert research notebooks and prototypes into production APIs, scalable microservices, or front‑end experiences.
• System design & architecture: Comfortable with distributed systems, containerization, scaling, observability, and resilience.
• ML/GenAI literacy: Understand how to select the right model type and integrate into a business workflow.
• Agentic workflows: Experience deploying multi‑agent systems and monitoring their behavior in production.
• Observability engineering: Hands‑on with Prometheus, Grafana, OpenTelemetry, and logging/metrics pipelines.
• Engineering rigor: CI/CD, testing, version control, tracing, blue/green deployments, and secure coding practices.
• Research & evaluation: Evaluate new frameworks, GenAI tools, and emerging technologies for practical adoption.
• Collaboration: Strong teamwork, mentorship, and communication, enabling strong cross‑functional delivery.

Skills

End-to-end engineering

Productizing ML

System design & architecture

ML/GenAI literacy

Agentic workflows

Observability engineering

Engineering rigor

Research & evaluation

Collaboration

Education

Bachelor’s or Master’s in CS/Engineering

Tools

FAISS

Pinecone

Weaviate

Milvus

LangChain

CrewAI

LangGraph

AutoGen

AWS

React/Next.js/Angular

Python

Node.js

Java

C#

Go

Neo4j

RDF/SPARQL

Snowflake

Job description

We are looking for a Senior Machine Learning Engineer with strong software engineering fundamentals who can seamlessly integrate Machine Learning and GenAI capabilities into scalable, production-grade applications.

This role is ideal for an experienced engineer (10+ years) who enjoys building end‑to‑end systems—including front-end, back-end, APIs, and ML‑powered services—rather than doing research.

You will partner closely with ML researchers, data scientists, and platform teams to transform prototypes, RAG pipelines, and agentic workflows into robust systems used across the enterprise.

The Expertise We’re Looking For

• Bachelor’s or Master’s in Computer Science, Engineering, or related technical field.
• 10+ years of professional software engineering experience (full stack, distributed systems, APIs, cloud).
• 4–6 years working with machine learning or GenAI systems in production environments.
• Experience designing and deploying RAG pipelines with embeddings and vector search.
• Hands-on with vector databases: FAISS, Pinecone, Weaviate, Milvus, etc.
• Experience with agentic frameworks such as LangChain, CrewAI, LangGraph, AutoGen.
• Strong expertise in cloud-native engineering (AWS preferred).
• Proficient in modern front-end frameworks (React/Next.js, Angular, or similar) and in building robust web applications.
• Back-end development using Python (advanced) and one or more of: Node.js, Java, C#, or Go.
• Familiarity with traditional ML models, their applications, and how to integrate them into products.
• Cloud Platforms: AWS services such as S3, Lambda, ECS/EKS, SageMaker, API Gateway.
• Databases: Experience with relational systems (Oracle), cloud data warehouses (Snowflake), vector DBs, and knowledge graphs (Neo4j, RDF/SPARQL).
• DevOps: CI/CD pipelines, Docker, Kubernetes, GitHub Actions, infrastructure-as-code.
• Understanding of Responsible AI, data privacy, and ethical considerations in ML-driven systems.
• Good to have: Experience with model monitoring, evaluation frameworks, and automated alerting.
• Ability to communicate architectural decisions and technical concepts to non-technical partners.

The Skills You Bring

• End-to-end engineering: You build full-stack applications that integrate ML/GenAI services seamlessly.
• Productizing ML: Skilled at converting research notebooks and prototypes into production APIs, scalable microservices, or front‑end experiences.
• System design & architecture: Comfortable with distributed systems, containerization, scaling, observability, and resilience.
• ML/GenAI literacy: You understand how to select the right model type and integrate it appropriately into a business workflow.
• Agentic workflows: Experience deploying multi‑agent systems and monitoring their behavior in production.
• Observability engineering: Hands‑on with Prometheus, Grafana, OpenTelemetry, and logging/metrics pipelines.
• Engineering rigor: CI/CD, testing, version control, tracing, blue/green deployments, and secure coding practices.
• Research & evaluation: Ability to evaluate new frameworks, GenAI tools, and emerging technologies for practical adoption.
• Collaboration: Strong teamwork, mentorship, and communication, enabling strong cross‑functional delivery.

The Value You Deliver

• You build the full-stack components—APIs, UIs, services that bring ML and GenAI capabilities directly into business applications.
• You create scalable, secure, and reliable platforms for RAG, model serving, and agentic applications.
• You improve engineering productivity by providing reusable patterns, libraries, and frameworks for ML integration.
• You help the organization understand where different model types (LLMs, embeddings, classifiers, ...) best fit business's challenges.
• You collaborate closely with ML researchers, product teams, and cloud engineering to deliver measurable business impact.
• You elevate the team by sharing…..??