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Data Scientist, Applied Ai

hace 2 semanas

Desde casa, México Azumo A tiempo completo

Azumo is currently looking for a highly motivated **Data Scientist / Machine Learning Engineer** to develop and enhance our data and analytics infrastructure. The position is **FULLY REMOTE**, based in Latin America.

This position will provide you with the opportunity to collaborate with a dynamic team and talented data scientists in the field of big data analytics and applied **AI**. If you have a passion for designing and implementing advanced machine learning and deep learning models, particularly in the **Generative AI** space, this role is perfect for you. We are seeking a skilled professional with expertise in **Python** for production-level projects, proficiency in machine learning and deep learning techniques such as **CNNs** and **Transformers**, and hands-on experience working with **PyTorch**.

We’re looking for a versatile **Machine Learning Engineer / Data Scientist** to join our big-data analytics team. In this hybrid role you’ll not only design and prototype novel **ML/DL models**, but also productionize them end-to-end, integrating your solutions into our data pipelines and services. You’ll work closely with data engineers, software developers and product owners to ensure high-quality, scalable, maintainable systems.

**Key Responsibilities**:
**Model Development & Productionization**
- Design, train, and validate supervised and unsupervised models (e.g., anomaly detection, classification, forecasting).
- Architect and implement deep learning solutions (CNNs, Transformers) with **PyTorch**.
- **Implement Retrieval-Augmented Generation (RAG) pipelines and integrate with vector databases.**:

- Build robust pipelines to deploy models at scale (**Docker**, **Kubernetes**, **CI/CD**).

**Data Engineering & MLOps**
- 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.

**Software Development Best Practices**
- 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.

**Cross-Functional Collaboration**
- Communicate analysis, design and results clearly in English.
- Partner with DevOps, data engineering and product teams to align on requirements and SLAs.

At **Azumo** we strive for excellence and strongly believe in professional and personal growth. We want each individual to be successful and pledge to help each achieve their goals while at **Azumo** and beyond. Challenging ourselves and learning new technologies is at the core of what we do. We believe in giving back to our community and will volunteer our time to philanthropy, open source initiatives and sharing our knowledge.

**Requirements**:
**Minimum Qualifications**
- 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 Qualifications**
- 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.

**Benefits**
- Paid time off (PTO)
- U.S. Holidays
- Training
- Udemy free Premium access
- Mentored career development
- Profit Sharing
- $US Remuneration