Data Scientist, Applied AI - Remote

Azumo

8h•Remote

About The Position

Azumo is seeking a highly motivated Data Scientist / Machine Learning Engineer to develop and enhance their data and analytics infrastructure. This is a FULLY REMOTE position based in Latin America, requiring professional English proficiency (B2/C1). The role offers the opportunity to collaborate with a dynamic team and talented data scientists in big data analytics and applied AI, focusing on designing and implementing advanced machine learning and deep learning models, particularly in the Generative AI space. The ideal candidate will have expertise in Python for production-level projects, proficiency in machine learning and deep learning techniques (CNNs, Transformers), and hands-on experience with PyTorch. This hybrid role involves designing, prototyping, and productionizing ML/DL models end-to-end, integrating them into data pipelines and services, and working closely with data engineers, software developers, and product owners to ensure high-quality, scalable, and maintainable systems.

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.

Nice To Haves

Experience with cloud platforms (AWS, GCP, or Azure) and their AI-specific services like Amazon SageMaker, Google Vertex AI, or Azure Machine Learning.
Familiarity with big-data ecosystems (Spark, Hadoop).
Practice in CI/CD & container orchestration (Jenkins/GitLab CI, Docker, Kubernetes).
Exposure to MLOps/LLMOps tools (MLflow, Kubeflow, TFX).
Experience with Large Language Models, Generative AI, prompt engineering, and RAG pipelines.
Hands-on experience with vector databases (e.g., Pinecone, FAISS).
Experience building AI Agents and using frameworks like Hugging Face Transformers, LangChain or LangGraph.
Documentation skills using PlantUML or similar.

Responsibilities

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.