Analytics Engineer

• Partner with Sales and senior leadership on strategic customer opportunities with AI labs, model development teams, and enterprise AI organizations.
• Lead discovery conversations with technical, research, product, operations, and executive stakeholders.
• Translate customer requirements into clear solution designs, workflows, pilot plans, proposals, and implementation approaches.
• Design solutions across model evaluation, multilingual data, speech/audio, transcription, expert review, post-training data, and quality workflows.
• Support RFPs, RFIs, proposals, pricing assumptions, and Statements of Work.
• Create customer-facing materials including solution narratives, workflow diagrams, pilot structures, and technical explanations.
• Work closely with Delivery, Quality, Supply Chain, and Technology teams to ensure proposed solutions are feasible, scalable, and commercially sound.
• Support clean handoff from Sales to Delivery, including requirements, assumptions, risks, and success criteria.
• Build reusable solution templates, scoping frameworks, and best practices for AI data and evaluation programs.
• Stay close to industry trends in model evaluation, benchmarking, RLHF, post-training data, agentic workflows, and multilingual AI.

Senior Solutions Engineer, AI Data & Model Evaluation Solutions

• Set the direction Own the long term direction of the Intelligence offering, spanning data models, analytics, and customer facing insights.
• Partner with Product to build a roadmap for our data science offerings.
• Provide insights and what-if analysis tooling for our C-Suite customers.
• Establish the architectural principles, analytical standards, and ways of working that allow Intelligence to scale safely and credibly.
• Design and evolve the data architecture and canonical data model that underpins Intelligence, building on an existing warehouse.
• Develop and ship data science work: predictive signals, pattern detection, automated insights: that genuinely moves the needle for customers.
• Design and deliver dashboards that customers trust and rely on.
• Own analytics engineering end to end: modelling data, optimizing queries, versioning work, and reviewing changes before they reach customers.
• Introduce tooling and processes that speed up development while improving quality and consistency.
• Work hand in hand with Engineering to ship Intelligence features into the product.
• Engage directly with customers to understand reporting needs, explain insights, and validate value.
• Support Commercial and Operations teams with insights that underpin upsell, renewal, and confident decision making.

Senior Analytics Engineer, Intelligence

• Design, develop, install, deploy, test, medium-scale analytics models and AI applications in support of business and client outcomes and objectives.
• Design and implement training pipelines for machine learning models.
• Build leading MLOps pipelines to deploy and update machine learning models and analytics applications.
• Build leading feature engineering pipelines extracting and preparing data for machine learning model performance.
• Train and evaluate models using diverse datasets and performance metrics, choosing and applying appropriate machine learning algorithms.
• Maintain and optimize model to improve accuracy and monitor reliability.
• Implement leading monitoring mechanisms to track model performance and identify issues.
• Detect and mitigate biases in machine learning models to promote fairness.
• Write leading specifications of machine learning models and processes for reproducibility and maintenance.
• Present insights and performance tuning recommendations from machine learning models to inform business decisions.
• Promote ethical principles and responsible AI guidelines for machine learning development.
• Integrate machine learning models into production environments.
• Develop and deploy APIs for model access and prediction requests.
• Leverage best practices and patterns in MLOps (machine learning operations) to ensure efficient and scalable model lifecycle management.
• Integrate leading AI service and machine learning models with existing applications and DevSecOps deployment pipelines.
• Embrace Agile development practices and employ SDLC processes on all development.
• Collaborate with data scientists practitioners, finance and business analysts, actuaries and engineers to achieve model objectives.
• Effectively mentor other engineers.
• Lead a "data-first" approach to problem solving and designing IT solutions.

Analytics Engineer jobs in Ireland

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Senior Solutions Engineer, AI Data & Model Evaluation Solutions

Senior Analytics Engineer, Intelligence

Principal ML Analytics Engineer