Top Artificial Intelligence Jobs in Brazil

Supports financial data extraction, quality checks, dashboard creation, report automation, metric documentation, forecasting preparation, and process documentation. The intern will use SQL, visualization tools, AI, and automation technologies to improve financial routines and solve business problems while learning in a data-focused SaaS environment.

Supports accounts payable and receivable, invoice processing, bank reconciliations, collections monitoring, cash flow tracking, financial close, budgeting reports, KPI monitoring, and leadership presentations. The intern also manages financial documentation, software subscriptions, vendor contracts, process documentation, and identifies opportunities to automate repetitive finance tasks using AI, scripts, and modern tools.

Manage Lynx’s Brazilian marketing strategy by localizing global messaging, executing B2B demand generation campaigns, coordinating industry events, creating commercial content, managing social media, and supporting Sales and Pre-Sales. The role owns vendor coordination, competitor monitoring, brand consistency, and performance measurement through pipeline, lead, event, and engagement metrics. It requires strong knowledge of Brazil’s fintech and financial services market, autonomous execution, executive communication, and occasional travel across Brazil and Latin America.

Manages and expands a mid-market account portfolio, combining existing customers and new prospects. Leads consultative, strategic B2B sales from prospecting through close, develops territory plans, forecasts pipeline, builds relationships with C-level and operational stakeholders, and positions Salesforce’s portfolio across Sales, Service, Marketing, Commerce, and AI. The role requires disciplined pipeline management, achievement of sales quotas, navigation of complex sales cycles, and strong customer satisfaction and advocacy.

Design, build, deploy, and maintain AI-powered applications, LLM agents, RAG pipelines, RPA solutions, and workflow automations. Integrate models with business systems, APIs, and data sources; monitor performance, cost, latency, and accuracy; and improve production solutions. Partner with stakeholders to identify use cases, document implementations, and support adoption while applying responsible AI, privacy, security, and compliance practices.

Leads the design and delivery of secure, scalable, LLM-enabled applications spanning APIs, user interfaces, orchestration pipelines, vector databases, and enterprise integrations. Builds reusable AI tooling, SDKs, and governance processes; deploys solutions across cloud environments; ensures observability, operational excellence, and responsible AI compliance. Partners with business stakeholders, architects, security teams, and strategic vendors to prioritize high-value use cases, guide platform strategy, and deliver production-ready systems.

Staff AI Engineer in dLocal’s AI Lab responsible for scouting, prototyping, benchmarking, and evaluating emerging AI technologies. Builds evaluation harnesses and experimental systems for LLMs, agents, retrieval, and orchestration; assesses quality, cost, latency, security, and reliability; and produces adoption recommendations. Defines guardrails and readiness guidance, coordinates production hand-offs, supports governance, mentors engineers, and communicates findings to technical and non-technical stakeholders.

Own paid acquisition across Google, Bing, Meta, and YouTube, scaling campaigns from creative production through launch, optimization, and attribution. Produce high-volume ad assets using AI tools, manage full-funnel campaigns, improve campaign processes, automate repetitive work, and analyze CAC, ROAS, payback, and product activation data. The role requires hands-on Google Ads expertise, creative production skills, subscription funnel experience, and strong ownership in a remote startup environment.

Lead the design and operation of an AI-driven software development pipeline where agents implement changes and engineers govern risk. Own conventions, source indexing, artifact-based verification gates, agent identities, autonomy tiers, containment, intake, measurement, incident records, and continuous rule calibration. The role requires deep CI/CD and platform expertise, strong software engineering and testing judgment, security instincts, hands-on coding-agent experience, cross-functional collaboration, and excellent English communication.

Lead client engagements delivering Microsoft Fabric data and AI solutions from discovery through production deployment. Responsibilities include workshops, assessments, architecture design, platform implementations, migrations, data pipeline development, Spark workload optimization, lakehouse and medallion architecture, governance, and stakeholder communication. The role requires independent consulting delivery, technical decision-making, and translating business requirements into scalable Azure-based data solutions with minimal oversight.

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.

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.

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.

Serve as the technical expert for Codex across customer portfolios, designing and scaling AI-enhanced software development workflows. Build demos, integrations, prototypes, automations, and reference implementations; lead technical workshops and enablement sessions; publish technical guidance; gather deployment feedback; and advise customers on architecture, operations, security, model configuration, and adoption strategy. Collaborate with Sales, Solutions Engineering, Product, and Applied Engineering to influence product direction and drive successful enterprise deployments.

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.

Designs and maintains secure, scalable cloud data pipelines for insurance use cases including underwriting, risk management, fraud prevention, and customer experience. Integrates structured and unstructured data, builds data lakes and warehouses, automates ingestion and quality processes, and implements governance, lineage, and regulatory compliance. The role also develops datasets for machine learning and analytics while collaborating with data scientists, architects, and business stakeholders.

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.

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.

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.

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.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Build and operate scalable batch and streaming data pipelines powering AI systems. Own ingestion, storage, transformation, serving, monitoring, validation, and continuous improvement across the data lifecycle. Partner with AI engineers and researchers to translate model requirements into reliable production infrastructure, using data lakes, warehouses, relational, graph, and vector databases. Define data strategy and ensure high-quality, observable, timely data for AI models and agents.