Top Machine Learning Jobs in Rio de Janeiro

The VP of Product Development will lead a global engineering team for eDiscovery platforms, focusing on modernization, reliability, and AI integration while managing execution and stakeholder communication.

Design and deploy a multi-agent orchestration layer for conversational AI, build production-grade Python ML services, lead end-to-end ML projects, establish team best practices, mentor engineers, and ensure secure, scalable, high-performance customer-facing AI systems.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.

Lead end-to-end supply chain transformation engagements, advising executives and directing multidisciplinary teams to improve planning, procurement, logistics, inventory, and supplier performance. Design future-state architectures, build business cases, drive implementation, grow client relationships, and scale practice capabilities while delivering measurable operational and financial impact.

Lead end-to-end supply chain transformation engagements for commercial and utility clients. Advise executives, design future-state supply chain architectures, oversee implementation across planning, procurement, logistics, and analytics, build financial value cases, and manage multidisciplinary teams to deliver measurable operational and financial results.

The intern will develop AI models, enhance neural mechanism understanding, and collaborate on generative modeling while maintaining code quality.

Work with client-provided data to clean, validate, preprocess, and engineer features. Train and tune machine-learning models (avoid overfitting), optimize runtime, and communicate results to stakeholders. Collaborate with client teams and internal Product/Research to deploy and improve risk-detection solutions.

Own product direction for ML platform capabilities across the end-to-end ML lifecycle. Set vision and roadmap, define standards and tooling, drive adoption with data science and engineering teams, deliver templates/CLIs/workflows, and improve model deployment, observability, and operational practices to enable scalable, reliable ML solutions.

Work with client data to develop fraud-detection models: clean and preprocess data, engineer features, build and tune ML models, create scalable pipelines, collaborate with clients and internal teams, communicate results, and contribute technical leadership and product enhancements.

Design and implement recommender systems, manage ML pipelines, optimize models, and collaborate with teams to improve customer engagement.

Build and maintain production ML infrastructure: deploy, monitor, and scale models; create automated end-to-end pipelines; implement CI/CD for model releases; establish monitoring/logging/alerting; optimize system architecture for reliability, scalability, and cost; collaborate with data scientists and engineers to operationalize models.

As a Machine Learning Solutions Architect, you will design and implement data solutions, provide thought leadership, and ensure scalable deployment of machine learning models.

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

Drive development of AI products focused on Knowledge Graphs and LLMs, design architectures, integrate data, and build APIs.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

Design, deploy, monitor, and maintain ML models and ML-enabled systems across batch and real-time contexts. Build pipelines, features, tooling and platforms, collaborate with teams, and ensure operational and business performance of ML solutions.

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.

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

Lead the architecture and implementation of machine learning solutions, ensuring scalable and secure environments for data teams while collaborating with clients and various stakeholders.

The Principal AI/ML Architect leads client engagements, advising on ML architecture and strategies, overseeing ML systems, and ensuring technical quality for diverse customer needs with a focus on AWS technologies.

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.

Join the Advanced Accelerator Training Program to master AI/ML skills, engage in real projects, and prepare for impactful roles. Requires 4+ years of ML experience and proficiency in Python and relevant frameworks.

The Engineering Matcher connects clients with technical talent by analyzing resumes, conducting interviews, and maintaining a candidate pool while collaborating with stakeholders.

Seeking a Software Engineer with 5+ years in AI/ML to design and implement scalable web APIs and applications, focusing on generative AI solutions, while engaging cross-functional teams and mentoring junior developers.