Top Machine Learning Jobs in Brazil

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Leads J.P. Morgan’s Brazil Payments franchise, including Treasury Services sales, in-country strategy, client growth, product innovation, partnerships, operations, technology, risk management, regulatory representation, and talent development. Drives new business, market share, client wallet expansion, payments capabilities, and innovation roadmaps across liquidity, trade, merchant services, acquiring, acceptance, payment processing, and digital payments.

Staff Software Engineer responsible for designing and delivering highly scalable, resilient distributed systems supporting high-concurrency workloads, conversational AI, microservices orchestration, and real-time machine learning. The role requires technical leadership, architecture expertise, engineering trade-off analysis, collaboration, and inclusive culture building. Candidates should have 10+ years of software development experience, strong distributed systems knowledge, and fluency in Portuguese and English. Experience with LLMs, multi-agent systems, data pipelines, FinOps, or monolith modernization is advantageous.

Leads AML transaction-monitoring optimization, rule testing, threshold tuning, scenario design, and false-positive reduction. Drives FIU automation and workflow improvements, evaluates AI and LLM tools, supports alert triage and investigations, and partners with Product, Engineering, Operations, and vendors on compliance technology integrations. Helps scale monitoring frameworks across products, jurisdictions, and transaction volumes while maintaining regulatory alignment.

Own BI product strategy for Quote-to-Cash and Order-to-Cash financial systems. Partner with Finance, Sales, Legal, Product, engineering, and FinSys teams to identify operational friction, translate needs into BI and AI opportunities, author PRDs and roadmaps, validate commercial data, and ensure quoting, billing, contract, and revenue-recognition processes are accurately represented in downstream data products.

Build and optimize Palantir Foundry applications, scalable data pipelines, ingestion workflows, data models, and ontology-aligned structures. Develop mission-focused analytics, dashboards, and operational workflows for Army decision-making. Lead technical workshops, stakeholder requirements gathering, code reviews, repository management, deployment strategies, and DevSecOps practices. Integrate AI and machine learning models while ensuring secure, compliant, and maintainable solutions.

Leads Agentic AI and Generative AI initiatives from strategy and architecture through implementation and evolution. Designs AI-agent and multi-agent solutions using LLMs, enterprise data, APIs, and business systems. Provides technical leadership to multidisciplinary AI, data, software, and cloud teams, converts business needs into scalable AI products, advises executives, and establishes standards for architecture, security, governance, quality, and evaluation.

Lead data science initiatives supporting global Healthcare Compliance through machine learning, advanced analytics, automation, generative AI, and Copilot solutions. Responsibilities include developing and deploying models for risk detection, anomaly identification, and monitoring; building scalable data pipelines; analyzing structured and unstructured data; creating NLP, OCR, embeddings, chatbot, and knowledge retrieval capabilities; ensuring responsible AI governance; and advising stakeholders while leading projects and mentoring team members.

Develop and deliver AI-enabled solutions that translate business needs into practical applications. Build prototypes, predictive models, generative AI applications, agents, RAG workflows, APIs, and integrations using Python, SQL, Azure, and Snowflake. Evaluate solution quality, safety, cost, and reliability while supporting responsible AI, compliance, CI/CD, production readiness, monitoring, and documentation. Collaborate with analysts, engineers, architects, platform specialists, and business stakeholders in an Agile delivery environment.

Builds scalable software, data pipelines, and AI solutions for agricultural R&D analytics. Responsibilities include deploying machine learning models, developing cloud-native applications, managing production environments, integrating scientific data and domain logic, partnering with R&D teams, and evaluating agentic AI capabilities. The role also emphasizes responsible AI, governance, explainability, testing, security, and reliable production operations.

Support Mastercard Identity sales in Brazil by serving as the technical product expert for identity verification, fraud detection, and risk solutions. Lead discovery, solution design, proof-of-value testing, technical presentations, customer training, documentation, and onboarding. Explain machine learning, data science, APIs, and probability-based decisioning to technical and nontechnical stakeholders. Build customer relationships, attend conferences, provide market insights, and relay customer feedback to Product. Travel approximately 10%.

Owns global fraud detection strategy across markets and fraud vectors. The role develops rules, risk signals, machine learning models, automation, case management, and AI-powered detection capabilities. It establishes model governance, monitoring, KPIs, and human oversight while identifying emerging threats such as synthetic identities, deepfakes, and AI-enabled fraud. The strategist partners with Fraud Operations, Risk, Product, Engineering, and Data Science to implement scalable, continuously monitored fraud controls.

Lead the strategy, architecture, development, and operation of AI products for Syngenta’s global Vegetable Seeds business. Manage and mentor distributed engineering teams, contribute hands-on code, own AWS infrastructure, and ensure reliable, secure, ethical AI delivery. Collaborate with product, data, and business stakeholders to apply machine learning, generative AI, computer vision, and advanced analytics to supply chain, production, quality, and logistics challenges. Drive platform scaling, partner knowledge transfer, budgets, timelines, and deployment across global regions.

Leads federal sales strategy and growth for Department of War, Intelligence Community, and national security customers. Owns pipeline development, capture planning, forecasting, account strategy, complex pursuits, partner relationships, and executive customer engagement. Builds scalable sales processes and a future federal sales organization while translating AI security capabilities into mission outcomes. Collaborates on proposals, demonstrations, acquisition materials, product feedback, and go-to-market strategy. Travel is approximately 10–25%.

Develop and deploy predictive machine learning models for anomaly detection, forecasting, rate modeling, and invoice classification. Apply NLP, embeddings, and LLMs to supply chain documents and contracts. Build scalable data pipelines, monitoring systems, and retraining workflows; analyze transactional data; query databases with SQL; create visualizations and executive presentations; and maintain data quality, governance, security, and model performance.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Build and deploy full-stack features for a high-throughput eCommerce platform. Develop Python, Node.js, and TypeScript backend services, React frontends, cloud integrations, secure storage, monitoring, and audio plugin licensing pipelines. Collaborate with product and design teams, communicate technical solutions, and own projects from inception through deployment.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

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.

Design and deliver AI data solutions, including labeling pipelines, quality controls, automated workflows, and AI tooling. Serve as the primary technical contact for customers, lead discovery and solution design, manage end-to-end delivery, monitor quality and financial KPIs, troubleshoot issues, and optimize scalable solutions. The role requires strong Python and machine learning knowledge, project ownership, customer communication, and the ability to explain technical concepts to varied stakeholders.

Design, build, and optimize a multi-region, highly-available Java/Scala microservices data pipeline processing thousands of messages/sec. Improve performance, implement fraud-detection pipelines with ML (Kafka/Kafka Streams), deploy to multi-cloud Kubernetes, and push frequent production releases to maintain five-nines uptime.

Performs entry-level data science and consumer analytics work using Python, SQL, ETL, statistical modeling, machine learning, and data mining. Builds retention, agency economics, and lead optimization models; handles low-complexity projects; supports presentations and communicates technical findings. Executes basic to intermediate model deployments using established MLOps practices and collaborates with analytics and IT teams.