Machine Learning Engineer

EXL Talent Acquisition Team; Dublin; Hybrid; EUR 90,000 - 140,000; Full time

Benefits offered by this job

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 machine learning and GenAI systems powering products and client solutions. You will work with product, engineering, and client stakeholders to identify high-impact opportunities, prototype ideas, and scale them into secure production systems.

Expect to set ML engineering standards, from data quality to MLOps, and mentor junior engineers while collaborating across geographies.

Qualifications

• 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 and hands-on experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Practical experience building GenAI/LLM applications: prompt engineering, RAG pipelines, embeddings, vector databases, and LLM APIs (OpenAI, Anthropic, Bedrock).
• Solid grounding in ML fundamentals: supervised/unsupervised learning, feature engineering, model evaluation, and error analysis.
• Experience deploying and operating models on AWS (SageMaker, Bedrock, Lambda, ECS/EKS) or equivalent services.
• Working knowledge of MLOps tooling: MLflow, Weights & Biases, model registries, orchestration (Airflow, Kubeflow, Step Functions).
• Strong data skills: SQL, Airflow, relational and NoSQL stores; exposure to Spark is a plus.
• Experience exposing models as services: REST/GraphQL APIs, batch and real-time inference.

Responsibilities

• Design, build, and deploy machine learning models and GenAI solutions powering products and client solutions.
• Develop end-to-end ML pipelines from data ingestion to deployment and retraining, using orchestration tools like Apache Airflow.
• Build and productionize GenAI applications, including LLM integration and RAG pipelines.
• Fine-tune and optimize models for accuracy, latency, and cost.
• Deploy and operate models in production on AWS (SageMaker, Bedrock, Lambda) and containerized inference.
• Implement MLOps practices: experiment tracking, versioning, CI/CD for ML, monitoring for drift.
• Collaborate with data engineering to design robust feature stores and data pipelines.
• Establish evaluation frameworks for traditional and LLM-based models and ensure responsible AI practices.

Skills

Python

ML frameworks

GenAI/LLM

AWS

MLOps

Airflow

Kubeflow

Docker/Kubernetes

REST/GraphQL

Data engineering

Tools

Airflow

PyTorch

TensorFlow

SageMaker

Bedrock

Lambda

ECS/EKS

PostgreSQL

MySQL

Spark

pgvector/Pinecone/OpenSearch

Job description

EXL (NASDAQ: EXLS)is a global data and artificial intelligence ("AI") company that offers services and solutions to reinvent client business models, drive better outcomes and unlock growth with speed. EXL harnesses the power of data, AI, and deep industry knowledge to transform businesses, including the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect.

We are headquartered in New York and have more than 60,000 employees spanning six continents. For more information, visit www.exlservice.com .

Role Title: Machine Learning Engineer

Location: Dublin, Republic of Ireland (Flexible hybrid working)

Employment Type: Permanent

Summary of the role:

EXL Digital is looking for an experienced Machine Learning Engineer to join our team. At EXL, we believe there is always a better way. We look deeper, we find it, and we make it happen. We’ve built a culture founded on core values of innovation, collaboration, excellence, integrity, and respect.

In this role, you will design, build, and operate machine learning and GenAI systems that power our products and client solutions. You'll work with product, engineering, and client stakeholders to identify high-impact opportunities, rapidly prototype solutions, and harden them into scalable, secure, and cost-effective production systems. You'll also help set ML engineering standards across the team, from data quality and evaluation rigor to MLOps and responsible AI practices.

As part of your duties, you will be responsible for:

• Design, build, and deploy machine learning models and GenAI solutions that solve real business problems, from problem framing and data exploration through production deployment and monitoring.
• Develop end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and retraining, using orchestration tools such as Apache Airflow.
• Build and productionize GenAI applications, including LLM integration, prompt engineering, RAG (retrieval-augmented generation) pipelines, embeddings, vector databases, and agentic workflows.
• Fine-tune, evaluate, and optimize models (classical ML, deep learning, and LLMs) for accuracy, latency, and cost.
• Deploy and operate models in production on AWS (or comparable cloud platforms) using services such as SageMaker, Bedrock, Lambda, and containerized inference.
• Implement MLOps best practices: experiment tracking, model versioning, CI/CD for ML, automated testing, monitoring for drift and performance degradation.
• Work with large, messy, real-world datasets: design data pipelines, ensure data quality, and build robust feature stores in partnership with data engineering.
• Establish evaluation frameworks for both traditional models (precision/recall, AUC, calibration) and LLM-based systems (grounding, hallucination rates, task success metrics).
• Embed responsible AI practices: fairness, explainability, privacy, and security, into model development and deployment.
• Collaborate with product managers, architects, and client stakeholders to translate business requirements into ML solutions with measurable impact.
• Write clean, well-tested, production-quality code and participate in design and code reviews.
• Build proof-of-concepts to validate ML/GenAI approaches quickly, and harden successful experiments into production systems.
• Mentor junior engineers and data scientists on ML engineering best practices.
• Stay current with the rapidly evolving ML/GenAI landscape and recommend adoption of new models, frameworks, and techniques where they add business value.

Qualifications and experience we consider to be essential for the role:

• 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 and hands‑on experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Practical experience building GenAI/LLM applications: prompt engineering, RAG pipelines, embeddings, vector databases (e.g., pgvector, Pinecone, OpenSearch), and LLM APIs (OpenAI, Anthropic, Bedrock).
• Solid grounding in ML fundamentals: supervised/unsupervised learning, feature engineering, model evaluation, and error analysis.
• Experience deploying and operating models on AWS (or comparable cloud): SageMaker, Bedrock, Lambda, ECS/EKS, or equivalent services.
• Working knowledge of MLOps tooling: experiment tracking (MLflow, Weights & Biases), model registries, pipeline orchestration (Apache Airflow, Kubeflow, or Step Functions), and monitoring.
• Strong data skills: SQL, workflow orchestration with Apache Airflow, and experience with relational (PostgreSQL, MySQL) and NoSQL data stores; exposure to Spark or similar big‑data tools is a plus.
• Experience exposing models as services: REST/GraphQL APIs, batch and real‑time inference, and integration with backend applications.
• Solid software engineering fundamentals: version control, testing, CI/CD, containers (Docker/Kubernetes), and code review practices.
• Understanding of responsible AI concepts: bias, explainability, privacy, and security considerations in ML systems.
• Experience working in Agile/SCRUM environments and delivering iteratively.
• Excellent communication skills, with the ability to explain models, trade‑offs, and results to both technical and non‑technical audiences.
• Ability to work with stakeholders across multiple geographies.

As part of a leading global Data and AI company, you can look forward to:

• A competitive salary with a generous bonus, private healthcare, life assurance at 4 x your annual salary, income protection insurance, and a rewarding pension.
• At EXL, we are committed to providing our employees with the tools and resources they need to succeed and excel in their careers. We offer a wide range of professional and personal development opportunities. We also support a range of learning initiatives that allow our employees to build on their existing skills and knowledge. From online courses to seminars and workshops, our employees have the opportunity to enhance their skills and stay up to date with the latest trends and technologies.
• As an Equal Opportunity Employer, EXL is committed to diversity. Our company does not discriminate based on race, religion, colour, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, age, or disability status.
• At EXL, we offer a flexible hybrid working model that allows employees to live a balanced, healthy lifestyle while strengthening our culture of collaboration.

To be considered for this role, you must already be eligible to work in the Republic of Ireland.