Top Artificial Intelligence Jobs in Brazil

Design and deliver cloud-native, enterprise-grade microservices and REST APIs using Java and Python on Azure. Leverage Databricks, Spark, and Delta Lake, containerization (Docker/Kubernetes), CI/CD and DevOps practices. Apply AI tools to automate engineering workflows, improve code quality, mentor teams, and collaborate with stakeholders across onsite/offshore teams.

Design, build, and deploy production-grade generative AI systems (LLMs, RAG, agentic architectures). Develop backend services, containerized deployments, CI/CD, monitoring, prompt engineering, model tuning, and collaborate with cross-functional teams to integrate AI into enterprise systems.

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.

Support account delivery using AI-enabled workflows and structured systems. Translate client requests and Account Manager direction into clear tasks, track stakeholders, decisions and follow-ups, maintain Airtable/work systems, draft client-ready updates, coordinate stakeholders, and continuously improve processes and AI-driven workflows.

Design, build, and ship production AI systems for an AI-native ERP: own agentic workflows, RAG pipelines, model integration, infrastructure, and evaluation frameworks to turn LLMs into reliable, production-grade features for financial data.

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 as the startup's first in-house engineer: build production and prototype code across the stack, validate technical approaches with fast experiments, integrate devices and services, work hands-on with robotics and hardware-adjacent systems, and act as the primary technical voice for leadership while evolving into a technical lead.

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.

Lead cross-functional teams to design and implement AI-driven applications on GCP using React 18, TypeScript, and Python FastAPI; integrate AI via Google ADK; manage data in Firestore and Cloud SQL; use generative AI tools and prompt-engineering best practices; conduct architecture and code reviews; coordinate work with GitHub Projects and document via Claude Code.

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.

Design and implement agent frameworks and orchestration for enterprise clients, integrating LLMs, retrieval, tools, and APIs. Build multi-step reasoning, evaluate agent performance, maintain reusable reasoning templates, and guide engineering teams on orchestration best practices while collaborating with clients.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Own post-sale AI customer journey: lead multiple AI implementation projects, manage timelines and risks, coordinate engineering/data teams, drive customer relationships, identify expansion opportunities, and improve delivery processes and playbooks.

Design, develop, fine-tune, deploy, and monitor generative AI and ML models; build data pipelines and integrations; implement automation and agentic systems using tools like LangChain and REST APIs; evaluate emerging AI tools; collaborate with cross-functional teams and mentor junior engineers.

As an AI Tech Lead at Trafilea, you will design and build AI-driven architecture, mentor engineers, and ensure scalable solutions aligning with business goals.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Perform part-time remote data tasks to improve English AI outputs: collect, evaluate, and annotate data; conduct pairwise comparisons and counting tasks; tag and label objects across audio, video, images. Flexible, long-term freelance work based in Brazil.

The Backend Engineer will design and implement AI agents, build scalable backend services, optimize RAG pipelines, and manage vector databases while collaborating with cross-functional teams.

As an AI Process Forward Deployed Engineer, you will deploy enterprise workflows, improve systems, drive adoption, and collaborate across teams to enhance AI integration in business processes.

Design, develop, and deploy production-grade AI automation using LLMs, MCP servers, RAG, and knowledge graphs. Build integrations, backend logic, data pipelines, and prompt-engineered workflows. Collaborate across cloud, data, security, and business teams to deliver secure, scalable, and maintainable AI solutions.

The role involves developing full-stack applications for mental healthcare, utilizing TypeScript, Node.js, React, and AI tools. Candidates will own features end-to-end and collaborate with teams to enhance user experience and system performance.

As a Senior Software Engineer, you'll develop full-stack applications, design backend services, improve system performance, and utilize AI tools in a healthcare-focused team.