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Open nowPosted 218 days ago

Data Science Engineer (AI & Simulation) Data Science Engineer (AI & Simulation)

MindTech1 open role

Where
Medellín
Work mode
Remote
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Your applicationOpen nowData Science Engineer (AI & Simulation) Data Science Engineer (AI & Simulation)MindTech · Medellín

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Share of postings closed within
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  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 218 days ago

The posting

About Mindtech Mindtech connects you with global projects and international teams. You will work with high standards, real challenges, and room to grow. We are more than 90 professionals in Latin America and the US, building software from end to end. About the role Were hiring a Data Engineer who can bridge: Structured datasets with qualitative, unstructured human responses Modern LLM tooling (e.g. OpenAI APIs) Algorithmic thinking, evaluation, and production delivery This role sits between data science, applied AI, and engineering.

What Youll Work On: - Analyzing and transforming structured datasets with qualitative human responses - Designing algorithms that combine historical data + LLM outputs - Building simulation systems across demographics, intent, and experience - Working with prompt engineering, tool usage, and multi-step LLM workflows - Defining evaluation metrics and benchmarking outputs against real data - Shipping production pipelines in collaboration with product and engineering

Tools & Technologies Python SQL / PostgreSQL LLM APIs Async processing pipelines Experimentation and evaluation tooling

What Were Looking For - 4+ years experience in data engineering, applied ML, or AI engineering - Experience working with qualitative, unstructured human-generated data - Hands-on experience with LLM APIs in applied systems - Ability to design defensible, benchmarkable algorithms - Comfort deploying work into production environments - Strong communication and collaboration skills

Youll Thrive Here If Youve - Built applied AI systems used by real users - Combined historical data with model-generated outputs - Worked with evolving specs and ambiguous problem spaces - Enjoy turning qualitative data into reliable, explainable systems

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