Required:
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3+ years of hands-on Data Science or ML Engineering experience
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Demonstrated experience building GenAI applications — RAG systems, LLM-powered pipelines, or agent frameworks in production or near-production settings
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Strong proficiency in classical ML: regression, classification, and model evaluation
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Proficiency in Python; experience with cloud platforms (AWS, GCP, or Azure)
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Fluent in both English and Spanish — you'll work with US clients and collaborate across our LATAM offices.
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Strong communication skills; comfortable presenting to non-technical stakeholders
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Design and build agentic AI systems — multi-step reasoning pipelines, tool-use agents, and orchestration frameworks (LangChain, LlamaIndex, or equivalent)
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Develop and deploy RAG architectures over structured and unstructured data sources, including document retrieval, embedding strategies, and LLM integration
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Build and productionize regression, classification, forecasting, and clustering models to drive measurable business outcomes
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Evaluate and improve LLM-based systems using structured evaluation frameworks (LLM-as-a-judge, retrieval quality, hallucination detection)
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Translate ambiguous client challenges into concrete analytical hypotheses and solution designs
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Build scalable data pipelines for ingestion, transformation, and model serving
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Communicate findings and recommendations clearly to both technical and non-technical stakeholders
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Experience with computer vision (image classification, object detection, or multimodal models)
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Familiarity with vector databases (Pinecone, Weaviate, pgvector, or equivalent)
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Consulting or client-facing experience
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Knowledge of MLOps practices: model monitoring, versioning, CI/CD for ML
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Work on high-impact, real-world AI problems across industries from day one
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Be part of a founding remote team with direct access to US leadership
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Collaborative, technically rigorous culture with a strong knowledge-sharing ethos
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