Top Machine Learning Jobs in Brazil

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.

Support deployment and maintenance of fraud-detection solutions: assist with ETL configuration, detection implementation, feature rollout, pipeline optimization, and cross-team client engagement under senior data scientists.

Design, build, schedule, monitor, and optimize scalable data pipelines using Apache Airflow and cloud and big-data technologies. Collaborate with analysts, scientists, and engineers to ensure reliable, secure data delivery. Troubleshoot failures, maintain data architecture, document technical decisions, lead solution implementation, and mentor team members through coaching, pair programming, and design reviews.

Design, develop, evaluate, and deploy scalable AI and machine learning solutions for fraud prevention, chargeback risk, and payment risk. Build production-grade APIs, data pipelines, and services; manage CI/CD, monitoring, testing, and troubleshooting for ML systems. Collaborate with data science, engineering, MLOps, and business teams to deliver impactful solutions, while mentoring colleagues and improving AI development practices.

Architect, develop, and deploy machine learning systems for chatbot and search applications. Design experiments, analyze structured and unstructured data, improve models, and develop LLM-powered product features using prompt experimentation, agent evaluation, and AI guardrails. Provide technical leadership to data scientists and contribute to Data Science chapter decisions, reviews, and knowledge sharing.

Architect, develop, and deploy machine learning systems for chatbots, search, and other product features. Design experiments, analyze structured and unstructured data, improve models, and develop LLM-powered applications using prompt evaluation, agents, guardrails, and proofs of concept. Provide technical leadership within the Data Science chapter and contribute to technical decisions, reviews, and knowledge sharing.

Architects, develops, and deploys machine learning systems for chatbots and search. Designs experiments, analyzes structured and unstructured data, improves models, and applies scientific methods to LLM-powered applications. The role evaluates prompts, agents, capabilities, and guardrails while building generative AI proofs of concept. It also provides technical leadership and contributes to Data Science chapter decisions, reviews, and knowledge sharing.

Architects, develops, and deploys machine learning systems for chatbot and search applications. Designs experiments, analyzes structured and unstructured data, and develops LLM-powered applications using prompt experimentation, agent evaluation, guardrails, and generative AI proof-of-concepts. Provides technical leadership within the Data Science chapter and collaborates with product, design, engineering, and machine learning teams.

Build and deploy production-grade agentic AI and machine learning systems, including scalable backend services, data pipelines, RAG applications, and cloud infrastructure. Collaborate with ML engineers, data scientists, and clients to discover use cases, develop AI-powered products, optimize platform performance, and support deployments. The role also involves MLOps, model monitoring and versioning, automated testing, infrastructure management, research and development, and direct client engagement.

Partner with LATAM sales to lead technical discovery, deliver demos and POCs, design solution architectures, resolve technical blockers, and support integrations across Telnyx products (voice, messaging, wireless, networking, storage, and AI). Act as technical expert through the sales cycle and convey technical value to both technical and executive audiences.

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 a 0-to-1 product surface end-to-end, including vision, roadmap, specifications, shipping, metrics, and customer outcomes. Partner directly with customers on discovery, onboarding, growth reviews, and deployments. Collaborate with software, ML/AI, and data engineers on data pipelines, models, agents, and activation platforms. Prioritize customer requests, define measurable success criteria, and guide product decisions across teams and time zones.

Designs, develops, monitors, and delivers scalable, secure GenAI and machine learning solutions on AWS for a financial-services client. Collaborates with multiple teams to define business metrics, architectural solutions, functional and non-functional requirements, and technical documentation. Records architectural decisions, supports solution delivery, and leads team knowledge sharing and development. Advanced English proficiency is required.

Design, build, deploy, and optimize end-to-end AI systems for cybersecurity applications. Develop production LLM applications and AI agents, manage the full machine learning lifecycle, analyze large-scale unstructured security data, and improve system scalability, reliability, and observability. Collaborate with product, engineering, and security teams to turn AI research into user-facing platform features.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Leads Snowflake’s LATAM Solutions Engineering team in Brazil, managing sales engineers while supporting technical sales cycles, demonstrations, proofs of concept, customer presentations, and regional technical-win forecasting. Partners with sales, channel partners, product, engineering, marketing, and services teams to achieve quota goals, identify customer solutions, improve win rates, and expand into new territories. Responsibilities also include recruiting, interviewing, coaching, developing team talent, and advising revenue leadership on sales strategy and product needs.

Build machine learning systems that detect and de-identify sensitive information across text and image data. Develop data pipeline validation, adversarial test sets, regression gates, replacement pipelines, and audit processes. Measure precision, recall, leak rates, false negatives, and replacement consistency while investigating data quality issues across pipeline stages. The role requires strong Python, SQL, ML evaluation, privacy compliance knowledge, and experience with NER or similar detection systems.

Design, optimize, and deploy Retrieval-Augmented Generation systems and large language model applications for clients. Responsibilities include fine-tuning models, improving retrieval and generation performance, building GenAI product frameworks such as search, chatbots, and summarization tools, establishing ML infrastructure, writing scalable production code, and providing technical leadership on AI/ML opportunities.

Own product strategy, roadmaps, backlogs, feature development, testing, and performance analysis for mobile games. Collaborate with game leadership and engineering, art, distribution, and data science teams throughout the product lifecycle. Coordinate stakeholders, communicate updates, define product requirements, evaluate partnerships, and use customer insights, benchmarks, and analytics to improve game performance.

Leads a data engineering team responsible for building scalable safety solutions and data platforms. Owns technology roadmaps, architecture decisions, data pipeline design, cloud systems, data quality, monitoring, CI/CD optimization, and enterprise ETL/ELT. Recruits, coaches, and develops engineers while establishing engineering standards, testing practices, reusable frameworks, and reliable batch and streaming architectures supporting analytics and machine learning.

Build and deploy practical AI-powered applications for clients using Python, Generative AI, LLMs, APIs, and cloud platforms. Collaborate with customers, product teams, and engineers to understand challenges, prototype solutions, integrate AI capabilities, and move features into production. Responsibilities include application development, testing, debugging, deployment, system integrations, AI agent workflows, solution architecture, and exploring technologies such as RAG, vector databases, MLOps, and AI observability.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Designs and develops user-facing applications, reusable platform services, APIs, integrations, workflow applications, scientific tools, and AI-enabled capabilities for 3M’s enterprise R&D data platform. Translates business and technical requirements into scalable solutions, integrates enterprise systems, contributes to architecture and platform standards, and promotes secure, observable, maintainable software. Collaborates with engineering, data, governance, operations, product, and customer success teams. The role is onsite at least four days weekly in Sumaré, Brazil.

Own the AIOps product lifecycle, roadmap, backlog, standards, KPIs, and release delivery. Translate observability data into business value, lead self-healing automation and continuous improvement, reduce recurring incidents and MTTR, support FinOps, and coordinate cross-functional teams and technology vendors. Provide guidance and enablement to operations teams while ensuring platform adoption, reliability, security, capacity, and alignment with strategic objectives.