Top Artificial Intelligence Jobs in Brazil

Drive end-to-end high-ticket sales in AI and automation: prospect, conduct discovery calls, nurture leads, manage pipeline in CRM, and close deals. Execute high-volume outbound campaigns using cold calling, email, and LinkedIn while meeting revenue targets and refining outreach with leadership.

Design, develop, and deploy scalable AI/ML solutions using LLMs and generative AI. Build RAG systems, chatbots, automation workflows, and production model pipelines; collaborate with product and engineering teams, monitor and improve model performance, and document systems.

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

The AI Research Engineer will innovate in model compression for multimodal AI systems, focusing on reducing model size and computational cost while ensuring high performance across edge devices.

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 multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

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.

As a Senior AI Platform Engineer, you'll maintain and evolve ML infrastructure for scalable AI applications, drive MLOps best practices, and collaborate effectively across teams to standardize engineering excellence.

Lead pre-sales and delivery of OpenShift AI solutions: design and implement enterprise AI/ML platforms, drive MLOps adoption, architect training/serving/inference at scale, support Generative AI and LLM deployments, ensure governance, automation, and alignment with business and IT strategies.

Lead frontend architecture for Clara Builders (Next.js 15, React, TypeScript), extend backend/infra (Prisma/Postgres, deploy pipeline), work on the Cloudflare AI Gateway and agent infrastructure (Figma-to-code MCP, documentation agents, n8n automations), design and ship new AI tooling (OAuth clients, unified access), and raise technical standards across the pod through ownership and code review.

Build the data foundation and LLM analysis layer for a Creative Engine: ingest and structure multi-platform performance and sales data, run video analysis with Gemini and Claude, implement semantic search with pgvector, connect signals into a queryable intelligence layer, integrate platform APIs, and own full-stack features (Django + HTMX + Tailwind) with async pipelines (Celery + Redis).

Design, build, and maintain cloud data pipelines and Snowflake/dbt data models; develop AI-powered applications using LLMs, RAG, tool-calling, and agent workflows; create APIs and integrations; monitor performance, data quality, and costs; troubleshoot interconnected systems; apply security, governance, testing, and CI/CD best practices; partner with stakeholders to deliver scalable solutions.

Architect and productionize ML and GenAI solutions on Databricks, including RAG and agent systems, MLOps pipelines, model training/serving/monitoring, and advising customers and product teams on AI adoption.

As an AI Engineer, you will develop and implement internal automation workflows, integrating various tools and APIs to improve productivity in design and development processes, primarily using LangChain and related frameworks.

Lead discovery and design of enterprise AI solutions, configure AI agents and knowledge structures in PIPE44, guide client onboarding and adoption, measure outcomes, and collaborate with engineers to implement and improve AI operating models and governance.

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

Lead the engineering team at Luna Joy, designing scalable software solutions and mentoring junior engineers while ensuring system performance and compliance with regulations.

The GTM Engineer will design and build automated systems for the company's go-to-market engine, focusing on revenue technology and AI capabilities across various teams and markets.

The Senior AI Engineer will develop AI-driven solutions using LLMs, focusing on prompt engineering, integration, research, and data utilization for payment systems.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Lead identification, design, deployment, and measurement of AI-powered automation across operations. Build agent and workflow automations, partner with Product/Engineering/Commercial, create dashboards in Metabase, close data gaps, document processes, and evangelize AI tooling to increase operational efficiency.

Contract developer will use Claude Code to implement and refine AI-native features, translate specs into production-ready TypeScript/React code, review and improve AI-generated code, write automated tests, document APIs and architecture, and identify technical debt while meeting milestones.

Design, build, and operate AI-powered backend services and APIs for production LLM features. Ensure reliability, low latency, cost efficiency, observability, and event-driven architectures; review designs and help move experiments to production.

Lead design, deployment, and optimization of NVIDIA cuOpt GPU-accelerated routing solutions on Microsoft Azure. Architect scalable, resilient Azure infrastructures, tune GPU performance, establish monitoring and capacity planning, operationalize solutions with Data Science and Platform teams, and provide technical leadership for CUDA and high-performance computing best practices and DevOps automation.