Top Artificial Intelligence Jobs in Brazil

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.

Leads quality assurance for AI training data by managing review queues, auditing annotations, monitoring quality trends, coaching reviewers, clarifying guidelines, training new team members, identifying recurring errors, and escalating complex issues. The role also supports corrective actions and continuous improvement while ensuring accurate, consistent, and timely project delivery.

Coordinate workforce planning and resource deployment for more than 1,800 contributors across AI training projects. Allocate resources based on demand and capacity, maintain deployment and availability records, track onboarding and offboarding, identify shortages and conflicts, manage resource requests, coordinate cross-functional handoffs, and improve workforce planning workflows in a fast-paced, shift-based environment.

Leads AI data annotation teams through project delivery, translating client guidelines into workflows, setting production targets, monitoring quality and productivity metrics, coaching team members, managing staffing and deadlines, and resolving operational risks. Partners with QA and tooling teams to improve processes and maintain accurate, timely training data delivery in a fast-paced environment.

Serves as a technical advisor to enterprise customers modernizing endpoint and virtual desktop environments. Designs secure cloud architectures using Windows 365, Azure Virtual Desktop, Intune, Defender, and Entra ID. Leads discovery, demonstrations, workshops, proofs of concept, and technical close plans while partnering with sales, security, identity, engineering, and partners. Helps customers adopt cloud-native, identity-first, Zero Trust, and AI-enabled solutions.

Designs and deploys secure AWS cloud architectures, leads enterprise migrations and application modernization, and builds Generative AI and machine learning solutions using Bedrock, SageMaker, vector databases, and RAG. Acts as the primary technical leader for client engagements, advises executive stakeholders, develops proofs of concept, drives AWS adoption and account growth, and mentors engineering teams while creating reusable infrastructure and delivery frameworks.

Own and modernize a luxury travel company’s internal business systems, centered on Quickbase. Map data structures and workflows, build automations, integrations, dashboards, and internal tools, and apply AI to practical operational tasks. Maintain data integrity, security, auditability, and performance while collaborating with business stakeholders. The role requires strong database, API, programming, web development, automation, AI API, and independent remote-working skills, with significant overlap with Pacific Time.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

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.

Evaluate AI chatbots and language models through structured text and voice interactions. Assess model capability, safety, helpfulness, and language quality in Brazilian Portuguese, then submit ratings, written feedback, screenshots, or recordings. This remote, task-based independent contractor role offers flexible scheduling and work in batches. Candidates need native or expert Brazilian Portuguese, strong English, AI model experience, reliable equipment and internet, and must pass language and technical assessments.

Designs and operates cloud-native platform infrastructure, including IaC, networking, containers, IAM, CI/CD, observability, secrets management, disaster recovery, and serverless automation. The role supports secure multi-tenant environments for AI application development, improves platform reliability and developer experience, troubleshoots complex issues, and collaborates with AI engineering teams on automated deployment workflows.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

Drive new enterprise business and strategic account growth for FICO’s analytics and decisioning solutions in Brazil. Build executive relationships, create pipeline, structure and close complex multi-million-dollar SaaS deals, develop joint business plans, advocate for clients, and collaborate with internal teams. The role focuses on banking and financial services customers, including credit risk, fraud prevention, lending, and account management, with travel of up to 50%.

Own daily operations and quality assurance for an internal AI-powered growth platform. Test releases, rule changes, edge cases, and regressions; document and verify bug fixes; monitor platform and agent activity; manage approval queues; evaluate agent output; escalate risks; and maintain test plans, QA checklists, runbooks, and release notes.

Design and develop scalable full-stack applications, microservices, APIs, and AI-powered solutions using cloud platforms, modern programming languages, LLMs, agentic workflows, machine learning, and intelligent automation. Responsibilities span requirements gathering, architecture, development, deployment, monitoring, and maintenance, including AI agents, predictive analytics, personalized experiences, and enterprise integrations.

Design and build production AI applications, including advanced RAG pipelines, multi-agent workflows, and evaluation frameworks. Optimize system latency, throughput, scalability, resilience, and cost. Apply data science, machine learning, model monitoring, governance, and privacy practices. Provide technical leadership, mentor engineers, and collaborate with platform, data, AI, product, and consulting teams on robust AI-powered tools and integrations.

Designs and deploys production-grade AI agents, LLM applications, RAG systems, multi-agent workflows, and scalable AI pipelines. Builds Python SDKs, integrates enterprise platforms and identity providers, implements MLOps and LLMOps practices, and establishes safety, governance, evaluation, observability, and human-in-the-loop controls. Collaborates with engineering, data, and product teams to deliver secure, domain-specific AI solutions.

Support Microsoft customers and partners with cloud and AI adoption through technical pre-sales, solution demonstrations, cloud architecture, and delivery projects. Interns collaborate with experienced engineers, architects, specialists, customers, and partners; tailor technical messaging; identify cloud growth opportunities; contribute to secure and optimized solutions; and develop technical and business expertise through coaching, readiness materials, community sessions, and events.

Design and deploy multi-agent AI systems using LangGraph, optimize token usage and latency, integrate MCP-based tooling, configure LangFuse observability, build automated evaluation harnesses, and implement OWASP-aligned security guardrails. The role requires backend software engineering expertise in Python, production experience with agentic AI and LLM workflows, familiarity with major foundation models, Docker, Azure, and strong English communication.

Lead applied AI engineering across generative and discriminative use cases, including scalable content generation, gamification, benchmarking, analytics, and multimodal behavior classification. Own AI-first SDLC practices, MCP and ML tooling, prompting standards, application benchmarks, roadmap development, and end-to-end deployment and maintenance of AI systems. Establish engineering standards for quality, scalability, and AI-first development while collaborating closely with product and engineering leadership.

Design, build, deploy, and continuously improve production-grade AI solutions, including generative AI, intelligent agents, RAG architectures, enterprise integrations, LLMOps, observability, and monitoring. The role defines technical architectures and standards, evaluates AI models, establishes quality and reliability metrics, leads complex initiatives, and serves as a technical reference for scalable, secure, cost-effective AI systems.

Own the full accounting cycle, including month-end close, general ledger, revenue recognition, reconciliations, AP, payroll, tax, audit readiness, forecasting, and financial reporting. Manage and develop a small accounting team, maintain accounting processes and schedules, oversee billing and collections, and improve automation using AI workflows and modern finance systems.

Own quality for web, mobile, API, AI-powered, and traditional features. Design and execute deterministic and evaluation-based tests, validate AI outputs for accuracy, safety, robustness, and relevance, perform manual and regression testing, document defects, and help establish QA practices for emerging AI technologies.

Lead and mentor 5–10 technical trainers creating and evaluating terminal-based AI training tasks. Review software, computational, electrical engineering, circuit, HDL, simulation, and automated grading tasks for correctness, reproducibility, numerical accuracy, performance, and reliability. Identify flawed assumptions, nondeterminism, race conditions, testing weaknesses, and technical inaccuracies. Allocate work, resolve blockers, provide detailed feedback, track revisions, and maintain technical documentation and quality standards.

Owns a portfolio of U.S. client accounts, driving retention, renewals, revenue expansion, executive relationships, forecasting, commercial negotiations, and delivery alignment. The role grows accounts through engineering staffing, AI delivery, advisory, and governance services while maintaining margins and account health. It requires consultative engagement with technology executives, credibility in software delivery and enterprise AI, and fluency in SOWs, MSAs, pricing, and forecasting.