Top Artificial Intelligence Jobs in Brazil

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.

Build and own end-to-end RAG and inference infrastructure: chunking, embeddings, retrieval optimization, vector DB schema/indexing, LLM integrations, agentic workflows, APIs (Python/FastAPI), and CI/CD/observability for a production legal research engine.

Evaluate AI-generated antitrust analysis for legal accuracy, build evaluation frameworks and instructions, test edge cases and jurisdictional errors, and write evaluation scripts using AI coding tools. Learn competition law from senior team members and access Concurrences' case law database.

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.

Lead IT operations across six Latin American countries, owning strategy, cloud and infrastructure, cybersecurity governance, service delivery, budgets, vendor management, and regional IT team development.

Design difficult open-web research problems for benchmarking frontier AI browsing agents. Responsibilities include constructing multi-step questions, validating clues through government and institutional sources, tracing evidence across archives and PDFs, documenting auditable citations, testing search difficulty, eliminating ambiguity, and delivering reproducible research documentation.

Build and launch AI-powered prospecting agents, SDR copilots, lifecycle automations, onboarding flows, and internal tools. Integrate CRM, marketing, sales, workflow, and LLM platforms; improve lead scoring, routing, personalization, activation, retention, meetings, and pipeline. Rapidly prototype, measure outcomes, collaborate with Sales, Lifecycle, Data, and Engineering teams, and scale solutions that demonstrate business impact.

Designs and delivers Azure cloud-native and AI solutions for enterprise customers. Leads architecture from requirements through production, removes technical blockers, and coordinates customers, partners, and internal teams. Develops production-grade software, CI/CD pipelines, security practices, infrastructure as code, and observability capabilities using agile methods. Facilitates architecture and design sessions, manages technical risks, supports migrations and refactoring, and co-develops strategic solutions involving cloud, AI, microservices, containers, and serverless technologies.

Own the full lifecycle of AI-native products, from discovering business challenges and designing solutions through development, deployment, adoption, and continuous improvement. Build secure, scalable full-stack applications, APIs, integrations, workflow automation, and AI-enabled products using modern cloud technologies. Partner with stakeholders to deliver measurable business outcomes, establish success metrics, create reusable engineering standards, and mentor teams on AI-native development and solution architecture.

Design and deploy predictive models and AI workflows to improve sales, retention, and revenue. Integrate and validate cross-system enterprise data, build APIs and scalable data products, investigate data anomalies, and communicate insights to technical and non-technical stakeholders to support decision-making.

Own the architecture, deployment, reliability, and cost optimization of production AI/ML infrastructure on GCP. Build model serving systems, reproducible ML pipelines, GitOps workflows, and multi-tenant Kubernetes platforms. Manage LLM and agentic workloads, Terraform infrastructure, observability, SLOs, drift monitoring, accelerator capacity, and inference costs. Define operational standards, improve automation, and guide platform evolution for scalable enterprise-grade ML systems.

Architect and scale AI-powered automation across Clara’s operations. The role maps workflows, prioritizes AI opportunities, builds agent- and rule-based systems using N8N and LLM APIs, develops Metabase dashboards, identifies data gaps, and partners with Engineering on Databricks pipelines. It advises senior leaders across Product, Sales, Finance, and Engineering, establishes AI operations best practices, supports feature launches, and documents scalable processes. This is a strategic senior individual contributor role focused on creating a team of AI systems rather than managing traditional staff.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Define the company-wide automation strategy, operating model, governance, architecture, security, observability, and lifecycle standards. Design and implement production-grade AI agents, RAG workflows, integrations, RPA solutions, and scalable AWS deployments while remaining hands-on with coding, reviews, troubleshooting, and production support. Establish responsible AI and LLMOps practices, optimize costs, mentor engineers, and partner with business leaders to deliver measurable efficiency, compliance, reliability, and data-quality improvements.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Owns the organization’s AI enablement strategy, governance, adoption, and quality standards. Responsibilities include developing role-based training, leading AI champion networks, managing tool access and intake, translating business needs into technical briefs, governing documentation and evaluation standards, maintaining reusable prompts and agents, defining AI KPIs, and establishing responsible-use guardrails. The role requires hands-on AI solution experience, LLM evaluation expertise, adoption leadership, strong knowledge architecture, KPI management, and executive stakeholder influence.