Top Machine Learning Jobs in Sao Paulo

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.

Lead the architecture and development of scalable AI/ML pipelines and production systems, including LLMs and Generative AI. Mentor engineering teams, guide technical decisions, operationalize models, optimize performance and costs, and implement MLOps practices such as CI/CD, monitoring, versioning, and maintenance. Partner with executives, clients, product managers, data scientists, and software engineers to align AI roadmaps with business goals and integrate ethical AI capabilities into enterprise applications.

Own fraud performance across cards, ACH, and tokenized wallets. Diagnose fraud vectors, design product-based controls, run experiments (A/B tests), partner with Data Science and Engineering, and maintain orchestration rules to reduce fraud while minimizing friction for legitimate customers.

Own fraud performance across cards, ACH, and tokenized wallets by diagnosing payment-frail vectors, designing product-first controls, running A/B experiments, partnering with Product/Engineering and Data Science, and maintaining orchestration/rule logic to reduce fraud while minimizing friction.

Responsible for managing a team of cloud engineers, overseeing technical tasks, engaging with clients, mentoring team members, and ensuring technical accountability on projects.

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

Design and scale agentic multi-agent systems that connect LLMs to internal APIs and tooling. Build production Python microservices, implement RAG, fine-tuning, and prompt engineering, and develop evaluation pipelines for LLM agents. Integrate vector databases and MCP, collaborate with product and conversation design, and ensure responsible, compliant AI-powered customer support experiences at scale.

Design, optimize, and deploy Retrieval-Augmented Generation systems and large language model applications for clients. Responsibilities include fine-tuning models, improving retrieval and generation performance, building GenAI product frameworks such as search, chatbots, and summarization tools, establishing ML infrastructure, writing scalable production code, and providing technical leadership on AI/ML opportunities.

As a Principal Technical Recruiter, you'll lead high-level searches while integrating AI into recruiting workflows, manage client relationships, and coach on using the AI recruiting platform, Moon Engine.

Build and maintain internal web applications and tools (dashboards, search, annotation, account management) with scalable, secure front-end code. Lead projects, reduce technical debt, collaborate across time zones, and deliver well-tested, extensible solutions that explain ML outputs and improve authentication workflows.

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.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Provide L2 application support: troubleshoot and resolve production issues, analyze logs and root causes, manage incidents and change processes, automate monitoring/health restoration, build runbooks, and produce health and trend reports to improve application stability.

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.

As the Head of Credit for Brazil, you will develop credit policies, lead analytics teams, build risk models, and collaborate across teams to drive growth and ensure quality in credit offerings.

Lead end-to-end delivery of Google Cloud adoption engagements. Coordinate cross-functional teams and executive stakeholders, scope and plan projects, mitigate risks, track adoption and utilization, integrate change management, mentor teams, and drive successful enterprise cloud migrations and modernization.

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

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, 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.

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.

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.