Top Machine Learning Jobs in Brazil

Create and review realistic, locale-specific robotics and natural sciences tasks in Brazilian Portuguese; apply scoring rubrics; evaluate and score AI and human responses for accuracy and regulatory alignment; produce gold-standard solutions and expert feedback.

The Machine Learning Engineer collaborates to design, deploy, and maintain ML models for medical imaging, ensuring production readiness and optimization.

Design, develop, and deploy machine learning pipelines. Implement MLOps practices and optimize models. Collaborate on data pipelines and cloud deployment.

Develop and deploy data and AI solutions for financial risk and customer experience. Own the full machine learning lifecycle, including problem definition, model design, training, productionization, monitoring, and impact measurement. Collaborate with Operations, Engineering, Product, Fraud, Compliance, and Credit teams using statistical methods and strong software engineering practices.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Own the architecture, development, automation, and production deployment of end-to-end machine learning solutions. Manage the ML lifecycle, evaluate cloud architectures for performance and cost efficiency, integrate with data engineering and software teams, troubleshoot complex systems, align solutions with business needs, and mentor teams on MLOps adoption.

Design and evaluate memory architectures for foundation models across modalities. Define memory performance metrics, build production-ready scalable learning models, collaborate with researchers and engineers, and recommend roadmap improvements based on model assessments.

Design, build, and deploy ML models and end-to-end pipelines; collaborate with data and MLOps teams to integrate, monitor, retrain, and optimize models; implement cloud-based scalable solutions and document processes.

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

Lead design and delivery of end-to-end ML pipelines and platform architecture for BEES AI. Standardize model development, ensure reproducibility, implement observability for training and production, and optimize scalable, cost-effective deployments while collaborating with data, platform, and product teams.

Implement and extend ML platform capabilities for training and inference; develop model development workflows; build observability for models; optimize deployments for cost and SLA; troubleshoot pipelines and share solutions.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

Design, build, and deliver enterprise-grade Generative AI/ML platform services, reusable frameworks, APIs, and agentic applications. Implement scalable model deployment, inference, monitoring, CI/CD, event-driven streaming (Kafka), and secure microservices while collaborating with architects, data scientists, and stakeholders to ensure governance, scalability, and operational excellence.

The AI/ML Engineering Manager will lead a team of ML engineers, manage client engagements, shape architecture, and drive pre-sales while ensuring team growth and performance.

Design, build, and maintain scalable ML models and data pipelines to automate logistics processes. Own end-to-end projects from problem definition to production deployment and monitoring. Collaborate with engineering, mentor ML teammates, review code, and evaluate tooling to improve infrastructure and workflows that deliver customer value.

Client-facing architect role designing and delivering production Applied AI solutions (predictive ML, MLOps, generative AI, LLM apps, RAG, agentic workflows). Lead technical workstreams from discovery through production, advise on cloud/data/AI tech choices, ensure reliability, observability, governance, and support sales and delivery activities.

Develop and implement LLM workflows, collaborate with teams on AI/ML solutions, ensure quality delivery using CI/CD practices, and support technical duties.