Top Artificial Intelligence Jobs in Brazil

Develop and maintain AI-enabled tools, automation, services, and workflows for hardware systems verification engineering teams. Integrate LLMs, agents, retrieval systems, and AI capabilities with internal infrastructure, repositories, knowledge sources, and enterprise platforms. Build secure environments with guardrails and access controls to protect proprietary data. Support engineering users and improve productivity through responsible AI adoption.

Build and deploy production-grade agentic AI solutions across Palantir Foundry, Azure, and Databricks. Responsibilities include designing modular agent skills, extending enterprise ontologies, engineering data pipelines, integrating and optimizing LLMs, developing CI/CD pipelines, and delivering use cases from experimentation through production while collaborating directly with clients.

Build and maintain PHP and Laravel backend services and APIs for a major product release. Own features from design through deployment and monitoring, integrate agentic AI workflows and LLM-based tooling, optimize performance and reliability, improve security, and mentor engineers. Collaborate independently with a cross-functional team on ambiguous technical challenges while delivering production-ready software.

Lead executive discovery, process and value-stream analysis, and requirements definition for data, AI, and GenAI transformations. Facilitate workshops, identify automation opportunities, define data and model requirements, support pilots and UAT, manage stakeholder alignment, and measure business outcomes. Serve as a trusted client advisor, use generative AI to accelerate business analysis, ensure deliverable quality, and coach project team members.

Own end-to-end technical implementations for airline clients, including full-stack development, data integrations, cloud infrastructure, deployment, troubleshooting, and ongoing operations. Build client-specific Adapter functionality using Python, TypeScript, React, SQL, AWS or Azure, and Terraform. Collaborate with data science and product teams, communicate directly with clients, analyze revenue-management recommendations, and contribute to shared product architecture and roadmap decisions.

Design and optimize model-serving architectures and inference pipelines for high-throughput, low-latency AI performance on mobile, edge, and GPU-cluster environments. Develop custom GPU kernels and Metal shaders, benchmark production systems, diagnose computational and memory bottlenecks, and implement techniques including parallelism, quantization, pruning, FlashAttention, KV caching, and speculative decoding. Collaborate cross-functionally to integrate scalable inference solutions and continuously improve performance.

Own the end-to-end AI-assisted content production operation, including intake, editorial selection, production, review, publishing, measurement, and searchable asset management. Partner with technical teams to build internal automation for content ingestion, moment scoring, caption and hook generation, approvals, scheduling, and quality control. Coordinate creative resources, improve production efficiency, track performance, and continuously automate manual workflows to scale high-quality content output.

Lead AI and operational intelligence programs for retail and consumer-brand clients. Own client relationships, discovery, benchmarking, business cases, technical specifications, contractor direction, implementation, adoption, and expansion across concurrent accounts. Translate business opportunities into deployed AI-enabled systems, guide engineers and analysts, and measure outcomes against financial and operational metrics. The role requires executive communication, startup adaptability, technical fluency, financial modeling, and direct accountability for client results.

Conduct research and engineering for production AI systems, including training and adapting large language models, developing algorithms, generating synthetic data, optimizing inference, and evaluating and deploying AI capabilities. The role focuses on agentic behavior, coding, cybersecurity decision-making, and translating research advances into scalable products while balancing model quality, latency, throughput, and cost.

Salesforce is hiring an Agentforce Sales Specialist to help organizations adopt and benefit from Agentforce, its AI-powered CRM technology. The provided posting contains limited role-specific responsibilities and no stated qualifications, compensation, travel expectations, or clearance requirements.

Lead the Brazil-based R&D and software engineering operation, aligning teams, priorities, capacity, and delivery commitments. Establish operating routines, governance, documentation, and knowledge sharing while driving AI adoption and productivity improvements. Develop engineering leaders, manage performance and succession, oversee budgets and vendors, and represent the local operation in global forums. The role requires technical depth, fluent English, agile delivery experience, and onsite work in Florianopolis with potential relocation.

Designs and develops user-facing applications, reusable platform services, APIs, integrations, workflow applications, scientific tools, and AI-enabled capabilities for 3M’s enterprise R&D data platform. Translates business and technical requirements into scalable solutions, integrates enterprise systems, contributes to architecture and platform standards, and promotes secure, observable, maintainable software. Collaborates with engineering, data, governance, operations, product, and customer success teams. The role is onsite at least four days weekly in Sumaré, Brazil.

Build Efekta’s LATAM customer engagement function from scratch. Own engagement communications, community initiatives, adoption campaigns, customer advocacy and reference programs, Voice of Customer insights, QBR/EBR materials, and stakeholder engagement with ministries, teachers, and implementation partners. The role supports retention, renewals, expansion, and scalable customer success across accounts while collaborating with Program Managers, Sales, and Operations.

Own the AIOps product lifecycle, roadmap, backlog, standards, KPIs, and release delivery. Translate observability data into business value, lead self-healing automation and continuous improvement, reduce recurring incidents and MTTR, support FinOps, and coordinate cross-functional teams and technology vendors. Provide guidance and enablement to operations teams while ensuring platform adoption, reliability, security, capacity, and alignment with strategic objectives.

Deploys, secures, and automates enterprise AI platforms including Claude, Microsoft Copilot, and Gemini. Owns connector and skill requests, configures permissions and access groups, reviews AI capabilities for security risks, and replaces manual configuration with Terraform and CI/CD pipelines. Partners with security, identity, governance, and business teams to ensure compliant, well-documented deployments across development, staging, and production environments.

Evaluates business workflows for AI automation, recommends build-versus-buy and no-code-versus-custom approaches, and configures AI agents and integrations. Translates requirements into specifications and user stories for engineering, supports custom solution development and UAT, validates agent performance, documents processes, trains users, and manages the AI automation backlog and outcomes.

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.

Develop reusable APIs, connectors, MCP tools, and service contracts; integrate AI agents with enterprise systems such as SharePoint, SAP, Databricks, and SQL. Build orchestration, state management, workflow automation, retries, and exception handling. Apply secure authentication, authorization, RBAC, API governance, and least-privilege controls while delivering reliable, scalable cloud-native integration utilities.

Design, architect, and deploy autonomous agentic AI systems using multi-agent frameworks, vector databases, monitoring tools, and cloud platforms. Lead technical architecture, mentor developers, build user interfaces, optimize production performance, and implement ethical AI safeguards. Collaborate with researchers, MLOps engineers, data scientists, and product teams while maintaining high standards for code quality, documentation, scalability, transparency, and reliability.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Design and build scalable cloud-native SaaS platforms and production-grade Generative AI and Agentic AI solutions. Responsibilities include developing autonomous agents, Graph-RAG architectures, Python APIs, microservices, data platforms, and secure cloud infrastructure on Azure or AWS. The role leads solution architecture, technical roadmaps, architectural standards, AI governance, and cross-functional collaboration while remaining hands-on with development and mentoring engineering teams.

Own and grow revenue across assigned financial services accounts by developing business plans, building executive relationships, managing pipelines and forecasts, leading large transactions, coordinating internal specialists, and exceeding subscription, renewal, professional services, and training sales goals. The role requires enterprise software sales experience and knowledge of data management, data warehousing, AI use cases, containers, Kubernetes, and public cloud infrastructure.

Conduct approximately 50 outbound calls daily to US tax and accounting decision-makers, generate and qualify at least 10 opportunities monthly, manage pipeline activity in Salesforce, follow established sales methodologies, collaborate with Account Executives on lead handoffs, and participate in coaching. The role requires selling SaaS solutions powered by data and AI to CPAs and tax professionals.

Build and ship production software in client technology stacks, including APIs, services, data models, interfaces, automated tests, CI/CD, and failure handling. Use AI coding agents daily while reviewing all generated code. Work with relational and non-relational databases, third-party integrations, cloud deployments, infrastructure as code, and compliance requirements. Collaborate directly with clients in English and deliver maintainable, tested software end to end.

Build and operate production LLM agents, orchestration layers, tool-calling systems, retrieval pipelines, asynchronous workflows, and evaluation frameworks. Improve agent reliability, retrieval quality, latency, cost, and observability through production investigation and experimentation. The role requires strong backend engineering, experience with RAG and search systems, durable workflow design, and the ability to explain architectural decisions and trade-offs.