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Applied AI Data Scientist

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Onsite

Applied AI Data Scientist

Acuity Analytics

Applied AI Data Scientist

Skills

SQL QueriesData EngineeringPythonData ScienceMachine LearningAI

2 months ago

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Important information


Contract type:

Permanent

Location:

Heredia Province, Heredia, Costa Rica

Work mode:

Onsite

Published on:

21 August 2026

What they need


We're looking for a Senior AI & Data Engineer (GenAI & LLM Evaluation) to help build and improve AI-powered data products.

Working alongside Data Scientists, Engineers, and Delivery teams, you'll develop scalable data pipelines, build GenAI evaluation frameworks, and deploy production-ready AI solutions. This is a hands-on role where you'll own solutions from design through to deployment while working on cutting-edge Generative AI technologies.

If you enjoy solving real-world problems and building practical AI solutions that make an impact, we'd love to hear from you.

Skills and experience required

Core skills

  • 5+ years' experience in Data Engineering, Machine Learning, AI Engineering, or Data Science.
  • Strong Python and SQL skills.
  • Hands-on experience with Generative AI and Large Language Models (LLMs).
  • Experience evaluating LLMs using golden datasets, model benchmarking, or LLM-as-a-Judge techniques.
  • Experience building and deploying production-ready ML solutions.
  • Strong understanding of data pipelines, validation, and quality frameworks.
  • Experience integrating APIs and cloud-based AI services.

Soft skills

  • Strong problem-solving and analytical skills.
  • Comfortable owning solutions end-to-end.
  • Excellent communication and stakeholder management.
  • Experience working in Agile, cross-functional teams.
  • Consulting or customer-facing experience is a plus.

What you will do

  • Build and maintain scalable data pipelines and workflows.
  • Develop evaluation frameworks for GenAI and LLM applications.
  • Create and maintain golden datasets for model testing.
  • Evaluate and benchmark LLM performance.
  • Build ML-assisted tools to improve delivery and automation.
  • Deploy production-ready AI solutions.
  • Work closely with delivery teams to solve technical challenges.
  • Continuously improve data quality, automation, and operational efficiency.

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