Machine Learning Engineer

EXL

Machine Learning Engineer building production ML and GenAI systems at EXL, a global data and AI company. Developing cloud-based pipelines, LLM applications, and MLOps practices.

Posted 8/19/2026full-timeDublin • 🇮🇪 IrelandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying machine learning models and GenAI solutions, with a strong focus on MLOps practices and cloud deployment on AWS. Proficient in building end-to-end ML pipelines and ensuring data quality while embedding responsible AI principles.

Highest-signal resume keywords

Machine Learning Model DevelopmentGenAI Application DevelopmentAWS DeploymentMLOps PracticesPython Programming

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenAIPythonScikit-learnPyTorchTensorFlowSQLDockerKubernetesFeature Engineering

Soft Skills

Excellent CommunicationCollaborationMentoring

Tools & Technologies

AWS SageMakerAWS LambdaApache AirflowREST APIsGraphQL APIs

Industry Keywords

MLOpsAgileSupervised LearningUnsupervised LearningData Engineering

Tech Stack

Tools & technologies

AirflowApacheAWSCloudDockerGraphQLKubernetesNoSQLPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, build, and deploy machine learning models and GenAI solutions for real business problems
Develop end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and retraining
Build and productionize GenAI applications involving LLM integration, prompt engineering, RAG pipelines, embeddings, vector databases, and agentic workflows
Fine-tune, evaluate, and optimize classical ML, deep learning, and LLM models for accuracy, latency, and cost
Deploy and operate models in production on AWS or comparable cloud platforms
Implement MLOps practices including experiment tracking, model versioning, CI/CD, automated testing, and monitoring
Design data pipelines, ensure data quality, and build feature stores with data engineering
Establish evaluation frameworks for traditional and LLM-based systems
Embed fairness, explainability, privacy, and security into model development and deployment
Collaborate with product managers, architects, and client stakeholders to translate business requirements into measurable ML solutions
Write clean, tested, production-quality code and participate in design and code reviews
Build proofs of concept and harden successful experiments into production systems
Mentor junior engineers and data scientists
Stay current with ML/GenAI developments and recommend valuable models, frameworks, and techniques

Requirements

What you’ll need

Minimum 5 years of software or data engineering experience, with at least 3 years building and deploying machine learning models in production
Strong programming skills in Python
Hands-on experience with scikit-learn, PyTorch, or TensorFlow
Practical experience building GenAI/LLM applications, including prompt engineering, RAG pipelines, embeddings, vector databases, and LLM APIs
Solid grounding in supervised and unsupervised learning, feature engineering, model evaluation, and error analysis
Experience deploying and operating models on AWS or comparable cloud platforms, including SageMaker, Bedrock, Lambda, ECS/EKS, or equivalent services
Working knowledge of MLOps tooling, experiment tracking, model registries, pipeline orchestration, and monitoring
Strong data skills, including SQL, Apache Airflow, relational and NoSQL data stores
Experience exposing models as REST/GraphQL APIs, batch and real-time inference, and backend integration
Software engineering fundamentals including version control, testing, CI/CD, Docker/Kubernetes, and code review practices
Understanding of responsible AI concepts including bias, explainability, privacy, and security
Experience working in Agile/SCRUM environments and delivering iteratively
Excellent communication skills for explaining models, trade-offs, and results to technical and non-technical audiences
Ability to work with stakeholders across multiple geographies
Must already be eligible to work in the Republic of Ireland

Benefits

Comp & perks

A competitive salary with a generous bonus
Private healthcare
Life assurance at 4 x your annual salary
Income protection insurance
Rewarding pension
Professional and personal development opportunities
Online courses, seminars, and workshops
Flexible hybrid working model