Azumo

Data Scientist, Applied AI

Azumo

full-time

Posted on:

Origin:  • 🇦🇷 Argentina

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Job Level

Mid-LevelSenior

Tech Stack

AirflowAWSAzureCloudDockerGoogle Cloud PlatformHadoopJenkinsKubernetesNumpyOpen SourcePandasPythonPyTorchScikit-LearnSpark

About the role

  • Design, train, and validate supervised and unsupervised models (e.g., anomaly detection, classification, forecasting).
  • Architect and implement deep learning solutions (CNNs, Transformers) with PyTorch.
  • Develop and fine-tune Large Language Models (LLMs) and build LLM-driven applications.
  • Implement Retrieval-Augmented Generation (RAG) pipelines and integrate with vector databases.
  • Build robust pipelines to deploy models at scale (Docker, Kubernetes, CI/CD).
  • Ingest, clean and transform large datasets using libraries like pandas, NumPy, and Spark.
  • Automate training and serving workflows with Airflow or similar orchestration tools.
  • Monitor model performance in production; iterate on drift detection and retraining strategies.
  • Implement LLMOps practices for automated testing, evaluation, and monitoring of LLMs.
  • Write production-grade Python code following SOLID principles, unit tests and code reviews.
  • Collaborate in Agile (Scrum) ceremonies; track work in JIRA.
  • Document architecture and workflows using PlantUML or comparable tools.
  • Communicate analysis, design and results clearly in English.
  • Partner with DevOps, data engineering and product teams to align on requirements and SLAs.
  • Design and prototype novel ML/DL models and productionize them end-to-end, integrating solutions into data pipelines and services.

Requirements

  • Bachelor’s or Master’s in Computer Science, Data Science or related field.
  • 5+ years of professional experience with Python in production environments.
  • Solid background in machine learning & deep learning (CNNs, Transformers, LLMs).
  • Hands-on experience with PyTorch or similar frameworks (training, custom modules, optimization).
  • Proven track record deploying ML solutions.
  • Expert in pandas, NumPy and scikit-learn.
  • Familiarity with Agile/Scrum practices and tooling (JIRA, Confluence).
  • Strong foundation in statistics and experimental design.
  • Excellent written and spoken English.
  • Preferred: Experience with cloud platforms (AWS, GCP, or Azure) and their AI-specific services like Amazon SageMaker, Google Vertex AI, or Azure Machine Learning.
  • Preferred: Familiarity with big-data ecosystems (Spark, Hadoop).
  • Preferred: Practice in CI/CD & container orchestration (Jenkins/GitLab CI, Docker, Kubernetes).
  • Preferred: Exposure to MLOps/LLMOps tools (MLflow, Kubeflow, TFX).
  • Preferred: Experience with Large Language Models, Generative AI, prompt engineering, and RAG pipelines.
  • Preferred: Hands-on experience with vector databases (e.g., Pinecone, FAISS).
  • Preferred: Experience building AI Agents and using frameworks like Hugging Face Transformers, LangChain or LangGraph.
  • Preferred: Documentation skills using PlantUML or similar.