Top Artificial Intelligence Jobs in Brazil

Designs and implements automation and AI solutions to transform Finance processes across Brazil, Spain, and Mexico. Responsibilities include mapping processes, identifying bottlenecks and control gaps, redesigning workflows, deploying AI use cases, reducing manual work, improving management information, and partnering with Finance, Technology, and Data teams to deliver scalable solutions. Success is measured through efficiency, accuracy, faster cycle times, stronger controls, and improved business insights.

Deploy and operationalize AI agents across global operations: build single and multi-agent systems, implement RAG/knowledge layers, evaluation, guardrails, observability, and own outcomes and budgets.

As an AI Engineer at Vega, you'll design and implement AI systems, develop LLM-based applications, and optimize AI performance for cybersecurity solutions.

Design, build, deploy and maintain production software systems that embed LLMs and agentic AI. Develop scalable services, APIs, integrations, RAG/agent workflows, prompt engineering frameworks, monitoring and CI/CD. Operationalise models, optimise cost/performance, and collaborate with data scientists and business teams to deliver measurable AI-enabled products and decision-support solutions.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Design, develop, and deploy production LLM-powered applications and APIs, implement RAG and document-processing workflows (OCR + LLM), build conversational interfaces, ensure reliability and fallbacks, and collaborate with product teams.

Design and run AI-enhanced sourcing campaigns, maintain structured talent pipelines in Google Workspace, conduct market research and competitor mapping, automate repetitive tasks, ensure accurate data and excellent candidate experience, and evolve sourcing strategies using free LLM and automation tools.

The AI Senior Engineer will develop systems that integrate vision and logic, extract data from complex documents, and build AI pipelines using frameworks like LangChain while ensuring efficient PDF processing and prompt engineering.

The AI Senior Engineer will design and develop ETL pipelines for graph ingestion, manage data ingestion at scale, ensure graph hygiene, and handle temporal relationships while focusing on performance tuning.

Design and maintain scalable Python backends, integrate generative AI (LLMs, RAG, fine-tuning) into client products, communicate with clients, ensure code quality, and document services.

Build and maintain end-to-end RCM web products and infrastructure supporting thousands of practices. Work across frontend and backend, scale web infrastructure, ensure seamless UX, collaborate in a fast-paced team, and deliver production-quality features using the stated tech stack.

Join the AI Special Forces team to provide high-quality customer support, troubleshoot technical issues, and collaborate with teams to enhance client satisfaction with AI technologies.

As an Onboarding Executive, you will facilitate the onboarding process for new clients, ensuring smooth implementation and effective communication while building AI assistants tailored to client needs.

You will onboard new accounts, support clients with AI tools, and assist in enhancing their business through technology.

Assist clients with AI-related technical issues, providing support, troubleshooting, and collaborating with internal teams to ensure customer satisfaction.

The IA Operations Executive aids in onboarding new client accounts, builds AI assistants, aligns client requirements, and ensures successful implementation processes.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

Support research and analysis across real estate and tech, build small prototypes and tools (Python or no-code), collect/clean data, coordinate with local stakeholders, and help move projects from concept to execution in a fast-moving AI startup. Part-time with opportunity for full-time conversion.

Transform concepts into interactive prototypes using AI design tools, then build production-ready full-stack applications. Emphasize frontend excellence, design system alignment, accessibility, performance, and scalable backend services. Collaborate with designers and innovation leads and document work in spec-driven, ticket-based workflows.

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.

Design, build, and deploy agentic AI agents and architectures for financial applications. Integrate LLMs, APIs, data sources, and enterprise systems on Google Cloud, ensure observability, security, reliability, and optimize for scale and performance while collaborating with product, engineering, data, and risk teams.

Lead end-to-end deployment of Telepatia AI products at hospitals: coordinate onboarding teams, manage stakeholder relationships, analyze adoption data, identify and mitigate implementation risks, translate field insights to product and engineering, and build the implementation playbook.

Design, build, and optimize Java-based backend microservices and RESTful APIs. Integrate relational and NoSQL databases, implement ORMs, write automated tests, improve CI/CD pipelines, ensure security and performance, and use generative AI tools to accelerate development and documentation in agile teams.

Design, build, and maintain production AI applications and pipelines (agents, multi-agent systems, RAG). Integrate LLMs and AI services, develop developer tooling, observability and evaluation frameworks, optimize retrieval and prompt strategies, and implement secure, scalable cloud deployments.

Design and deploy production-grade AI systems (RAG, graph retrieval, agentic architectures) to surface service knowledge for heavy-equipment technicians. Build pipelines for multimodal extraction, training-content synthesis, validation, and confidence scoring. Scale AI workloads on GCP/AWS, integrate LLM APIs, and collaborate with product and engineering to ensure reliability, security, and cost efficiency.