Top Machine Learning Jobs in Brazil

Join an agile team building HealthTech applications: design and implement scalable microservices, integrate ML and third-party systems, conduct code reviews, author technical documentation, debug and optimize systems, and own services over time while maintaining high code quality and delivery standards.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead detection and analytics research across cloud, identity, endpoint, network, and application environments. Embed security expertise into product and R&D, apply AI/LLM and ML techniques for scalable detections, analyze attacker techniques, measure detection quality, and contribute research outputs and tooling.

Lead a security governance and risk management team to define frameworks, drive compliance (ISO/PCI), manage audits and remediation, report KPIs to leadership, embed security into products and vendors, and run awareness and AI-driven automation initiatives.

Lead full-cycle technical recruiting for Data/ML, Platform, and Engineering; build predictable pipelines, design structured interview processes and scorecards, run high-signal screening, partner with senior leadership, and improve recruiting workflows to hire and retain high-performing talent.

Create executive-ready dashboards and visualizations, leverage AI/ML and automation to build data products, manage commercial master data, synthesize large datasets into scalable models, collaborate with local analytics and engineering teams, communicate commercial results to senior leadership, own and govern Commercial Performance data assets, and deliver ad-hoc analyses on pricing, market share, and volume performance.

Own machine learning models from handoff through production, including packaging, deployment, inference, monitoring, drift detection, alerting, and incident response. Review model design and evaluation methodology, identify leakage and pipeline issues, and extend a shared ML platform. Establish engineering standards across AWS infrastructure, SageMaker, feature stores, CI/CD, and Terraform while mentoring and reviewing colleagues’ work.

Develop and scale backend services, APIs, and front-end interfaces to integrate LLMs and ML into banking products. Implement Encompass and mortgage LOS integrations, ensure API security, deploy on cloud (Azure/AWS), and optimize performance, scalability, and maintainability.

Build and scale bank-grade AI products that integrate LLMs and ML into banking services. Design and implement backend services, APIs, and performant web frontends (React/Next.js). Ensure security (OAuth2, JWT), scalability, load balancing, and cloud deployment (Azure/AWS). Collaborate with product, design, and ML teams, participate in system design, and apply infrastructure-as-code and Kubernetes/Terraform practices.

The Senior Data Scientist will design and build scalable data products, develop machine learning models, and collaborate with various teams to drive impactful data solutions.

Join the Data Marketing Consulting team to execute data-driven marketing projects: deliver analytics solutions, collaborate with Activation/Creativity/Strategy, build client relationships, communicate results, research technical approaches, and contribute best practices to ensure business value for clients.

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.

Design, build, and deploy agentic LLM workflows that plan, call tools, maintain state, self-correct, and integrate with external systems. Implement monitoring, evaluation, guardrails, error handling, and human-in-the-loop checkpoints for safe, long-running multi-step agent executions.

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.

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.

Responsible for leading digital transformation through scalable and resilient architectures, integrating AI, ensuring security, and developing data-driven solutions for the organization.

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.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Sell HiddenLayer's AI security platform to federal agencies by developing account strategies, driving pipeline and revenue, leading technical and executive engagements, navigating federal acquisition and contracting, coordinating cross-functional teams, and supporting adoption, expansion, and renewals across the Intelligence Community, DoD, DHS, and civilian agencies.

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.