Top Artificial Intelligence Jobs in Brazil

Manage FICO’s strategic partner relationship across Latin America, owning regional growth, pipeline development, channel sales engagements, negotiations, and revenue growth. Build C-suite relationships, develop multi-year engagement roadmaps, coordinate internal and partner teams, lead co-marketing and co-sales initiatives, and connect FICO’s analytics, AI, decisioning, and software solutions to fintech and payments opportunities. The role is based in São Paulo and requires regular LATAM and periodic international travel.

Develop and operate Java-based cloud-native backend services, REST APIs, and microservices for Ericsson BOS products. Build and deploy services on Kubernetes across AWS and Azure, using CI/CD, GitOps, observability, and secure-by-design practices. Collaborate with architects, platform engineers, and SREs in a distributed Agile environment. Responsibilities include testing, code reviews, technical design, production support, service scalability, reliability, and continuous improvement. Python, AI/ML, mentoring, and technical roadmap contributions are beneficial.

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.

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 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-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.

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.

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 the Energy Industries operations function across the Americas, coordinating project execution, engineering, field service, supply chain, quality, planning, and continuous improvement. Establishes common operating standards, governance, performance routines, and controls across countries. Drives digital transformation through automation, analytics, technology deployment, and responsible AI use cases. Develops pilots, adoption plans, training, and scalable implementation models while improving productivity, quality, forecasting, cash, risk, and customer outcomes.

Own AI-driven product features from discovery through production, working across frontend, backend, APIs, integrations, and cloud services. Lead architecture and technical discovery, collaborate with clients and stakeholders, optimize application quality and scalability, and mentor engineers in AI-assisted development practices. The role requires strong full-stack software engineering experience, product ownership, communication skills, and expertise in React, Next.js, TypeScript, Node.js or Python.

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.

Lead enterprise delivery of Databricks-based data, analytics, and Generative AI solutions. Design production data pipelines, lakehouse architectures, RAG applications, ML platforms, and cloud-native systems. Drive legacy data migrations to Databricks, advise executive client stakeholders, identify platform expansion opportunities, and mentor engineering and consulting teams. The role combines hands-on engineering, solution architecture, customer engagement, and technical practice development.

Lead the full product lifecycle by owning roadmaps, translating business priorities into executable plans, defining features with engineering and UX/UI teams, and driving timely delivery. Conduct user research, establish product metrics, lead client demos, support go-to-market positioning, and align product development with broader innovation goals. Manage cross-functional teams, including ML engineers, while iterating products based on user feedback.

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.

Develop realistic backend coding challenges, including feature implementations and bug fixes, for AI training projects. Design deterministic verifiers, automated tests, and validation criteria; evaluate code for correctness, reliability, maintainability, performance, and security; identify edge cases and bottlenecks; and refine tasks based on project requirements. The role requires strong backend engineering, API development, debugging, optimization, code review, algorithms, and independent problem-solving skills.

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.

Develop and test software by translating requirements and user stories into robust designs and production code. Implement solutions across the software development lifecycle using CI/CD practices, collaborate with product and operations teams, and contribute to application architecture. The role values an architectural mindset, user-focused solutions, Agile methods, software testing, and familiarity with React, GCP, full-stack development, and AI-assisted coding.

Two-year rotational graduate program spanning engineering, IT, freight services, and digital intelligence. Participants contribute to projects involving locomotive systems, propulsion, controls, automation, software, data analytics, AI, cloud technologies, reliability, testing, and continuous improvement before transitioning into a permanent role. The program requires an engineering bachelor's degree, recent or upcoming graduation, strong English proficiency, travel availability, and prior academic or extracurricular experience.

Conduct software debugging, vulnerability analysis, penetration testing, security assessments, code refactoring, and backend optimization across diverse codebases. Implement and improve features using supported programming languages, apply secure development practices, document findings, and recommend practical improvements. Contributions will provide cybersecurity and software engineering insights for AI training datasets.

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.

The Technical Lead will design, build, and modernize IVR and Voice Bot platforms, providing technical ownership and leadership in a hands-on role within the Contact Center Technology department.

Designs challenging computational genomics and bioinformatics tasks for AI benchmarking. Responsibilities include creating reproducible multi-step workflows using biological datasets, Python, command-line tools, and bioinformatics libraries; developing reference solutions, validation methods, grading criteria, and ground truths; troubleshooting scientific and computational issues; documenting deliverables; and communicating requirements and progress to project leads and reviewers.