Top Machine Learning Jobs in Brazil

Lead and execute the AI automation strategy, redesign processes for automation, build AI systems, and mentor a high-performing AI team, facilitating cross-functional adoption.

Full-cycle enterprise sales role focused on acquiring and expanding accounts in Brazil. Evangelize Replit, run demos and hackathons, manage deals and CRM (HubSpot), collaborate with product/engineering, negotiate contracts, support customer adoption, and gather feedback. Travel up to 25%.

Senior technical leader providing high-level AI strategy, architecture direction, and long-term systems design. Delivers strategic artifacts (architecture reviews, AI integration strategies, roadmaps), advises executives, collaborates with product and engineering teams, and mentors technical leaders to improve scalability, resilience, and governance across platforms.

Build and deploy production ML solutions for fraud detection, AML and claim automation. Implement data pipelines from client extracts to the data warehouse, develop/tune/test models, automate business tasks in C#, report KPIs, and engage with clients and stakeholders.

Leads data science strategy across Mexico and Brazil, manages and mentors a distributed team, and remains hands-on building and deploying machine learning models. Establishes modeling standards, infrastructure, delivery practices, and technical roadmaps while partnering with credit leadership on underwriting, valuation, impact measurement, and P&L analysis. Requires strong applied machine learning expertise, prior data science leadership, business communication skills, and understanding of credit or underwriting.

Leads enterprise sales strategy and revenue growth for AI-powered eDiscovery, legal technology, compliance, analytics, and data intelligence solutions. Builds pipelines, manages complex consultative sales cycles, develops executive client relationships, expands strategic accounts, and positions responsible AI adoption. Partners with product, operations, legal, security, marketing, and executive teams on solution development, pricing, contracts, implementation, and market expansion across corporations, law firms, government, and regulated industries.

Supports business development and client proposals by researching healthcare business problems and developing innovative analytics solutions. Assists with predictive modeling, patient journey analysis, HCP targeting, and alerts using statistical and machine learning methods. Translates client needs into methodologies and communicates clinically and commercially meaningful findings. Manages assigned tasks and timelines, supports automation and standardization, and contributes to new offerings and team initiatives.

Leads digital innovation initiatives across customer, employee, factory, and product experience domains. Identifies opportunities, facilitates ideation workshops, prioritizes concepts, develops digital strategies, and executes proof-of-concepts using agile and Design Thinking methods. Collaborates with business, IT, vendors, and matrixed teams to create measurable value, influence stakeholders, evangelize emerging technologies, and advance enterprise digital transformation.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Join an agile team building HealthTech applications: design and implement scalable microservices, integrate ML and third-party systems, conduct code reviews, author technical documentation, debug and optimize systems, and own services over time while maintaining high code quality and delivery standards.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead detection and analytics research across cloud, identity, endpoint, network, and application environments. Embed security expertise into product and R&D, apply AI/LLM and ML techniques for scalable detections, analyze attacker techniques, measure detection quality, and contribute research outputs and tooling.

Lead a security governance and risk management team to define frameworks, drive compliance (ISO/PCI), manage audits and remediation, report KPIs to leadership, embed security into products and vendors, and run awareness and AI-driven automation initiatives.

Lead full-cycle technical recruiting for Data/ML, Platform, and Engineering; build predictable pipelines, design structured interview processes and scorecards, run high-signal screening, partner with senior leadership, and improve recruiting workflows to hire and retain high-performing talent.

Build and deploy LLM-powered agents, workflows, and production applications using templates, pipelines, guardrails, retrieval, and tool integrations. Develop evaluation suites, integrate customer systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within defined scopes. The role requires strong Python and TypeScript skills, API development, production LLM application experience, and expertise in prompt and context engineering.

Build and deploy LLM-powered agents, workflows, retrieval applications, and production integrations. Develop comprehensive evaluation suites, implement guardrails, integrate customer systems and data sources, and monitor cost and quality telemetry. Collaborate with business users to iterate rapidly within defined scopes while applying strong Python, TypeScript, API, prompt, and context engineering practices.

Create executive-ready dashboards and visualizations, leverage AI/ML and automation to build data products, manage commercial master data, synthesize large datasets into scalable models, collaborate with local analytics and engineering teams, communicate commercial results to senior leadership, own and govern Commercial Performance data assets, and deliver ad-hoc analyses on pricing, market share, and volume performance.

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.

Design AI-ready data models and build data warehousing, reporting, and analytics solutions. Develop and maintain ETL pipelines, dimensional models, and data integrations. Partner with product, data science, and analysts to translate business needs into technical requirements supporting ML and GenAI initiatives.

Own machine learning models from handoff through production, including packaging, deployment, inference, monitoring, drift detection, alerting, and incident response. Review model design and evaluation methodology, identify leakage and pipeline issues, and extend a shared ML platform. Establish engineering standards across AWS infrastructure, SageMaker, feature stores, CI/CD, and Terraform while mentoring and reviewing colleagues’ work.

Develop and scale backend services, APIs, and front-end interfaces to integrate LLMs and ML into banking products. Implement Encompass and mortgage LOS integrations, ensure API security, deploy on cloud (Azure/AWS), and optimize performance, scalability, and maintainability.

Build and scale bank-grade AI products that integrate LLMs and ML into banking services. Design and implement backend services, APIs, and performant web frontends (React/Next.js). Ensure security (OAuth2, JWT), scalability, load balancing, and cloud deployment (Azure/AWS). Collaborate with product, design, and ML teams, participate in system design, and apply infrastructure-as-code and Kubernetes/Terraform practices.

The Senior Data Scientist will design and build scalable data products, develop machine learning models, and collaborate with various teams to drive impactful data solutions.

Join the Data Marketing Consulting team to execute data-driven marketing projects: deliver analytics solutions, collaborate with Activation/Creativity/Strategy, build client relationships, communicate results, research technical approaches, and contribute best practices to ensure business value for clients.

Lead and scale EBANX's global AML program across multiple jurisdictions. Own KYC/KYP/KYE, transaction monitoring, sanctions screening, investigations and SAR/STR reporting. Manage correspondent bank and regulator relationships, conduct enterprise AML risk assessments, and report to executive leadership and the Board. Build and coach a high-performing AML team and partner with Product, Engineering, Legal, and Risk to embed risk-based controls while applying automation and analytics to improve effectiveness and customer experience.