GenAI ML Engineer - Production AI Systems

EXL; Dublin; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Competitive salary & bonus

Private healthcare

Life assurance 4x salary

Income protection

Pension

Hybrid work model

Job summary

EXL is seeking an experienced Machine Learning Engineer in Dublin to design, build, and operate ML and GenAI systems powering products and client solutions. You will collaborate with product, engineering, and client teams to identify opportunities, prototype quickly, and scale production systems with secure, cost-efficient deployments.

The role emphasizes setting ML engineering standards, data quality, evaluation rigor, and responsible AI practices while mentoring junior engineers and aligning

Qualifications

• Minimum 5 years software or data engineering experience.
• At least 3 years building and deploying ML models in production.
• Strong Python skills and ML frameworks experience.

Responsibilities

• Design, build, and deploy ML/GenAI models and pipelines.
• Develop end-to-end ML workflows: data ingest to production deployment.
• Implement MLOps: tracking, versioning, CI/CD, monitoring.

Skills

Python

PyTorch

TensorFlow

scikit-learn

GenAI/LLM apps

Prompt engineering

RAG pipelines

Embeddings

Vector databases

LLM APIs

SageMaker

Bedrock

Lambda

ECS/EKS

Apache Airflow

Kubeflow

Step Functions

SQL

NoSQL

Spark

REST APIs

GraphQL

Docker

Kubernetes

CI/CD

Git

Tools

AWS SageMaker

AWS Bedrock

Lambda

ECS/EKS

Apache Airflow

Kubeflow

Step Functions

Docker

Kubernetes

SQL databases (PostgreSQL/MySQL)

NoSQL databases

Spark

OpenAI API

Anthropic API

Bedrock API

Pinecone/OpenSearch

pgvector

REST/GraphQL

MLflow

Weights & Biases

Job description

EXL is seeking an experienced Machine Learning Engineer in Dublin to design, build, and operate ML and GenAI systems powering products and client solutions. You will collaborate with product, engineering, and client teams to identify opportunities, prototype quickly, and scale production systems with secure, cost-efficient deployments.

The role emphasizes setting ML engineering standards, data quality, evaluation rigor, and responsible AI practices while mentoring junior engineers and aligning