Data Scientist, Applied AI - Remote
- Role
- AI / ML
- Experience
- Senior
- Employment
- Full-time
Open to AR, BR, CO, MX, US only. Set where you work from to check your eligibility.
No BS summary
Data Scientist / Machine Learning Engineer with 5+ years of production Python, strong ML/DL background, PyTorch, CNNs, Transformers, LLMs and RAG. Must be based in Latin America and have professional English proficiency B2/C1.
Core skills
Required skills
Optional skills
Required languages
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. Professional English proficiency (B2/C1) 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 . 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 ). 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. About Azumo Based in San Francisco, California, Azumo is an innovative software development firm specializing in AI software development services . We help companies of all sizes build intelligent applications by combining expertise in data, cloud, and AI . Our talented AI developers are trusted to deliver Top AI Development services in Generative AI , intelligent automation, and custom machine learning solutions. At Azumo , we believe in professional and personal growth. As a recognized AI Development company , we support our engineers in mastering the latest technologies and delivering Top AI Development services worldwide. Our culture emphasizes collaboration, continuous learning, and solving complex problems with modern AI solutions. We believe in giving back to our community and will volunteer our time to philanthropy, open-source initiatives and sharing our knowledge. If you are qualified for the opportunity and looking for a challenge please apply online at Azumo/join-our-team or connect with us at people@azumo.co 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
What you'll do
- 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.
What they require
- 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 and their AI-specific services.
- Preferred: Familiarity with big-data ecosystems.
- Preferred: Practice in CI/CD & container orchestration.
- Preferred: Exposure to MLOps/LLMOps tools.
- Preferred: Experience with Large Language Models, Generative AI, prompt engineering, and RAG pipelines.
- Preferred: Hands-on experience with vector databases.
- Preferred: Experience building AI Agents and using frameworks.
- Preferred: 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
Azumo develops and enhances data and analytics infrastructure; this role supports analytics, live operations, player insights, publishing intelligence, and business decision-making across a gaming ecosystem.