Top Machine Learning Jobs in Brazil

Serve as a technical advisor within Expert Services to design and deliver bespoke integrations, data strategies, and custom implementations of the Braze platform. Drive complex technical projects, support onboarding and SDK/API integrations, troubleshoot strategic technical issues, document architectures, and collaborate with Sales, Product, Engineering, and Customer Success to maximize customer adoption and time-to-value.

Lead end-to-end AI deployment of OfferFit by Braze: design use cases, run technical workshops, manage technical setup and UAT, drive customer success, improve playbooks, and influence product roadmap.

Build and deploy statistical and ML models (especially demand forecasting) to improve sales forecast accuracy. Analyze data, create actionable insights, collaborate with stakeholders and regional teams, set analytics standards, monitor model performance, and deliver dashboards and visualizations for supply chain decision-making.

Lead and collaborate on SAP Basis solutions, using AI, automation, and analytics to prototype and integrate new capabilities. Support NLP, computer vision, and ML enhancements in business process automation, manage and guide teams, and contribute to cross-team decision-making.

Design, build and deploy scalable ETL/data ingestion pipelines on GCP to support ML workloads; migrate legacy warehouses; automate data flows; implement data governance, quality rules and compliance policies.

Lead strategy and lifecycle for AI- and data-driven products, defining vision, roadmaps, and AI-powered features (LLMs, RAG, agentic architectures). Drive product discovery, validate hypotheses, manage complex integrations (CRM, dialers), ensure responsible AI practices and compliance, partner with engineering and data teams, prioritize backlog, assess financial/operational AI costs, and communicate value to executives and stakeholders.

Design and deploy a multi-agent orchestration layer for conversational AI, build production-grade Python ML services, lead end-to-end ML projects, establish team best practices, mentor engineers, and ensure secure, scalable, high-performance customer-facing AI systems.

Design and maintain enterprise IT and data architecture focused on data governance, MDM, metadata/catalog, and data quality. Lead technical design, implement MDM and metadata solutions, enable AI-driven automation, collaborate with stakeholders, mentor architects, and recommend technologies to improve data discoverability, reliability, and business value.

Lead technical direction and hands-on development of C360: design and build large-scale ETL pipelines, profile-matching/entity-resolution, ML and graph-based algorithms, scalable microservices/APIs, and AI-ready data services while mentoring engineers and shaping platform architecture.

Analyze large federal and state economic datasets, automate data collection and scraping, and rapidly publish clear, data-driven reporting on market-moving indicators. Collaborate with reporters and visual journalists, file public-records requests, and use emerging technologies including AI to enhance real-time coverage.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

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.

Own fraud performance across cards, ACH, and tokenized wallets. Diagnose fraud vectors, design product-based controls, run experiments (A/B tests), partner with Data Science and Engineering, and maintain orchestration rules to reduce fraud while minimizing friction for legitimate customers.

Own fraud performance across cards, ACH, and tokenized wallets by diagnosing payment-frail vectors, designing product-first controls, running A/B experiments, partnering with Product/Engineering and Data Science, and maintaining orchestration/rule logic to reduce fraud while minimizing friction.

Responsible for managing a team of cloud engineers, overseeing technical tasks, engaging with clients, mentoring team members, and ensuring technical accountability on projects.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.

Lead and define architecture, oversee scrum delivery, delegate tasks, supervise system changes, conduct security audits, liaise with stakeholders, write reports, and hire/train technical staff for scalable backend and data/AI solutions.

Own strategy, execution, and adoption of ML-powered computer vision products. Drive stakeholder alignment, prioritize quarterly roadmaps, define epics/HLRs, run Agile ceremonies, partner with engineering/data teams to deliver scalable APIs, ML models, and BI dashboards, and report on model performance and product adoption.

Develop and maintain enterprise ontologies, semantic models, glossaries, and metadata frameworks. Define business entities, relationships, and rules; align with data owners and architects; govern ontology lifecycle; review processes and data flows; promote adoption across AI, reporting, and governance initiatives.

Design and scale agentic multi-agent systems that connect LLMs to internal APIs and tooling. Build production Python microservices, implement RAG, fine-tuning, and prompt engineering, and develop evaluation pipelines for LLM agents. Integrate vector databases and MCP, collaborate with product and conversation design, and ensure responsible, compliant AI-powered customer support experiences at scale.

Lead operational management and governance of commercial CRM and related platforms for Sales, Medical, Market Access, Marketing, and CX. Oversee platform reliability, release/change management, integrations, demand and stakeholder management, vendor relationships, and service SLAs. Drive CRM transformation, migrations, architecture, compliance, and continuous improvement while building and leading a global IT commercial systems team.

Responsible for leading digital transformation through scalable and resilient architectures, integrating AI, ensuring security, and developing data-driven solutions for the organization.

Lead LATAM transaction monitoring end-to-end: manage team performance, tune pre/post-transaction rules, partner on ML detection, oversee investigations and SAR/STR filing, coordinate regulators/audits, drive automation roadmap, and own KPIs and QA.

Mine and analyze internal and external datasets to create data-driven press hooks, visualizations, and social content. Collaborate with PR, data engineering, and growth teams to produce viral social posts, reports, and press materials using automation and AI tools to scale workflows.

Lead global smart manufacturing and automation strategy for Wound Closure & Healing. Drive controls, MES, IIoT, AI/vision, robotics, and Industry 4.0 initiatives; manage roadmaps, harmonize platforms across sites, serve as MES BPM, mentor teams, support business cases, and ensure quality, compliance, EH&S, and financial reporting. Travel ~20%.