Top Artificial Intelligence Jobs in Brazil

Build production-grade AI applications, backend services, agents, agentic workflows, and MCP integrations using Java and/or Python. Integrate LLM capabilities with enterprise tools, implement prompt orchestration and evaluation workflows, and optimize performance, reliability, cost, safety, and observability. Collaborate across engineering, product, and architecture teams while supporting experimentation, releases, incident resolution, documentation, and AI engineering best practices.

Build production-grade AI applications, agents, agentic workflows, and MCP integrations using Java and/or Python. Integrate LLM capabilities with enterprise tools and APIs while managing performance, reliability, cost, and safety. Design prompt orchestration, context management, tool calling, memory, and evaluation workflows. Collaborate across engineering, product, and architecture teams, troubleshoot production systems, improve observability, support releases, and document AI engineering best practices.

Build production-grade AI applications, agents, agentic workflows, and MCP integrations using Java and/or Python. Integrate LLM capabilities with enterprise tools and APIs while addressing performance, reliability, cost, safety, and observability. Design prompting, context management, tool calling, memory, and evaluation workflows. Collaborate cross-functionally on scalable AI engineering patterns, documentation, releases, experimentation, incident resolution, and production support.

Build production-grade AI applications, backend services, agents, agentic workflows, and MCP integrations using Java and/or Python. Integrate LLMs with enterprise tools, APIs, and business systems while managing prompts, context, tool calling, memory, evaluation, reliability, cost, and safety. Collaborate on scalable AI architecture, investigate production issues, improve observability, support releases, resolve incidents, and document engineering practices.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Lead lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to onboard commercial, finance, R&D, and real-world data domains. Ensure data quality, observability, lineage, governed access, masking, and row- and column-level security while enabling reliable AI-agent access to enterprise data.

Lead Turno's data and AI strategy and execution: own data pipelines (BigQuery/DBT), analytics (Segment/Mixpanel, Looker), deliver executive reporting and self-serve analytics, partner on product and marketing analytics, build AI/ML readiness (embeddings, vector/feature stores), and manage a growing data team as a player-coach.

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.

Leads complex data and AI consulting programs as the primary client and partner contact. Owns delivery outcomes, roadmaps, scope, budgets, margins, forecasting, risks, change control, and executive communications. Coordinates multi-geo teams and technology partners, coaches delivery professionals, manages ambiguity and trade-offs, and drives measurable business impact through Agile delivery.

Design, build, deploy, and operate production-ready AI and machine learning systems for enterprise customers. Translate architectures and requirements into tested code, data and model pipelines, APIs, and AI applications. Implement predictive ML, generative AI, LLM, agentic, and intelligent automation solutions with evaluation, monitoring, guardrails, security, and governance. Integrate solutions with enterprise platforms and contribute reusable accelerators, engineering standards, and technical playbooks while collaborating with architects, data scientists, engineers, stakeholders, and clients.

Design and build scalable cloud-native SaaS platforms using microservices, AI agents, Graph-RAG, high-performance APIs, and modern data architectures. Own solution architecture across infrastructure, deployment, databases, and data pipelines while guiding engineering teams and influencing product, sales, and customer stakeholders. Implement reliable AI safeguards, documentation, testing, and DevOps practices. The role is remote in Brazil and requires advanced English communication.

Leads technical pre-sales engagements for enterprise AI business solutions. Designs secure, scalable architectures; conducts demonstrations, proof-of-concepts, hackathons, and architecture workshops; advises customers on Microsoft Copilot, Azure AI, and Microsoft 365 adoption; collaborates with account, marketing, delivery, engineering, and partner teams; and guides AI applications and agents toward production deployment.

Build Neo, an autonomous infrastructure engineering agent, including planning and execution systems, cloud resource tools, context systems, feedback loops, evaluation frameworks, web workflows, and integrations with Pulumi’s MCP Server, CLI, SDKs, and Cloud. Develop reliable, safe production LLM systems using Python and TypeScript while collaborating across platform teams and maintaining a strong product focus.

Leads complex client-facing data and AI programs from strategy through delivery, coordinating multi-geo teams and partners. Owns roadmaps, budgets, margins, risks, escalations, utilization, change control, and executive communications. Partners with technical leaders to connect delivery with measurable business outcomes, identifies account expansion opportunities, and supports coaching and AI-augmented delivery practices. The role requires managing multiple customers and workstreams across cloud, analytics, data platforms, and AI/ML environments.

Lead pre-sales and implementation of Azure cloud and AI solutions, shape solution architectures, estimate staffing/delivery, run technical workshops, manage risks and escalations, and scale repeatable AI/app offerings (LLMs, RAG, agent patterns) while ensuring security, observability, and responsible AI practices.

Design and deliver production-grade applied AI solutions (LLM workflows, RAG, agentic automation). Lead client discovery, define specs for prompt engineering, measure model quality/ROI, advise on data and safe deployment, and contribute reusable AI assets.

Design, build, test, and iterate prompts and prompt chains for production LLM workflows. Implement evaluation harnesses, guardrails, and regression tests. Integrate LLM components with APIs, RAG pipelines, and structured outputs. Tune for accuracy, cost, latency, and safety. Partner with AI Specialists, document reusable patterns, and grow a library of AI accelerators.

Build agent-enabled applications, backend automation, governed data artifacts, platform services, and discovery interfaces. Design APIs, microservices, asynchronous Python services, AI workflows, MCP servers, and modern frontends. Collaborate with data engineering and governance teams, contribute to architecture and best practices, and deliver tested, scalable production software using cloud, serverless, and CI/CD practices.

Develop data strategies aligned with client goals, establish governance and data management frameworks, enable data products and monetization, oversee execution and risks, expand client engagements, and promote data literacy and organizational adoption. Support tailored solutions involving data platforms, visualization, AI models, and generative AI applications while collaborating with international, cross-functional teams.

Design mechanics and features for AI-powered party, social, and multiplayer games. Collaborate with artists and programmers, use playtesting and iteration to refine gameplay, and pitch creative concepts. The role requires knowledge of LLM-driven gameplay and conversational AI characters, strong communication skills, adaptability, and teamwork in a fast-paced environment.

Leads technical and writing teams delivering generative AI products, coordinating customers, data, product, and research teams. Tracks annotator metrics, manages labeling initiatives, creates prompts and responses, develops stakeholder guidelines, and trains teams on LLM and dataset best practices. The role includes customer interaction, quality assurance, and exposure to potentially toxic or sensitive content.

Annotate, evaluate, and curate Portuguese text, video, and geographic data to train and improve generative AI and machine learning models. Follow detailed guidelines, conduct basic research, maintain annotation quality, identify data patterns, provide process feedback, meet deadlines, and complete required training, client testing, and interviews. This is a full-time remote role in Brazil, with a one-time São Paulo office visit to collect equipment.

Build scalable software systems, internal platforms, tools, APIs, services, and infrastructure using Go and TypeScript. Solve complex architectural problems, support AI-native and agentic systems, work with Azure and AWS, adopt AI-powered development tools, and improve engineering reliability, performance, architecture, and developer experience. Collaborate with distributed technical teams in a fully remote environment.

Build and maintain production-grade agentic AI systems for an internal AI platform. Design AWS infrastructure, knowledge graph solutions using Neptune or Neo4j, and LLM-based reasoning and retrieval workflows. Troubleshoot integrated systems, collaborate across teams, influence technical direction, and support engineering best practices. The contract is expected to last 4–6 months, with possible extension, and requires strong Python, AWS platform engineering, and agentic AI expertise.

Build and maintain RAG infrastructure for hundreds of thousands of legal documents: chunking pipelines, vector DB integration, embedding strategy, semantic search, retrieval/reranking/LLM orchestration, evaluation tooling, API components, and deployable backend services. Ship multiple features quickly and collaborate with CTO and founder on architecture and domain-driven trade-offs.

Evaluate AI outputs for antitrust accuracy across jurisdictions; build rubrics, annotation guidelines, and tests; design AI instructions; run structured legal experiments; own domains like EU merger control and cartels; write evaluation scripts using AI coding tools.