Top Machine Learning Jobs in Rio de Janeiro

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.

Atuar end-to-end com dados: ingestão, tratamento, análise avançada e desenvolvimento de modelos ML/DL/NLP. Construir e automatizar pipelines ETL/ELT, realizar deploy e monitoramento de modelos em cloud, otimizar performance e integrar soluções com times de engenharia, produto e negócio para gerar impacto no negócio.

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.

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.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Design, develop, enhance, and support scalable backend products and services using Java. Collaborate on technical architecture, APIs, testing, code reviews, security, performance, and high-availability systems. Apply AI and LLM solutions, support data management and visualization, and work with cross-functional product teams on experimentation and business metrics.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Leads the architecture and implementation of complex AWS cloud data solutions. Defines client data strategy roadmaps, supports pre-sales engagements as a data engineering subject matter expert, mentors technical teams, interviews candidates, and establishes data standards and operating procedures. The role requires extensive experience across traditional and modern data platforms, including data modeling, databases, BI, governance, big data, machine learning, generative AI, DataOps, infrastructure automation, and CI/CD.

Leads complex digital strategy, product, platform, marketing, and AI-enabled projects from initiation through closure. Manages scope, budgets, roadmaps, resources, risks, delivery methodologies, and client communications. Oversees human review and guardrails for agentic workflows, facilitates stakeholder workshops, and leads cross-functional teams across strategy, design, engineering, QA, and data. Mentors project management staff, improves delivery processes, and ensures measurable business value and client satisfaction.

Lead the technical direction of an AI agent product, designing conversational AI, RAG pipelines, agent architectures, LLM evaluation, prompt management, monitoring, and observability. Develop proprietary machine learning models, productionize them with feedback loops, identify new AI use cases, and provide technical guidance across product teams. This hands-on role involves writing code regularly and partnering closely with Product in a remote Brazil-based environment.

Build and ship AI product features for Alti, including conversational AI, agents, automated routines, RAG pipelines, prompt strategies, and LLM evaluation systems. Establish monitoring and observability for AI features, help deploy ML models to production, and create continuous feedback loops. Collaborate with Product and Design to solve customer problems using AI and strengthen the team’s ML delivery practices.

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%.

Drive adoption, engagement, renewal, and expansion for a large portfolio of cybersecurity customers through one-to-one and scaled communications. Lead onboarding, webinars, digital customer journeys, health monitoring, and escalation resolution. Collaborate with Renewals, Sales, Product, and Customer Success teams to identify customer risks and scale successful engagement programs. Manage high task volumes while improving customer outcomes and retention.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Build and operate a governed people-data platform supporting workforce analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, privacy controls, and reliability practices. Own the platform’s AI-readiness and infrastructure roadmap, monitor performance, manage incidents, and partner with People Analytics, HR Technology, Security, Legal, and Enterprise Data teams. Translate approved requirements into secure, production-ready technical solutions using Workday and Azure-based data technologies.

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.

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.

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.