Top Machine Learning Jobs in Rio de Janeiro

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.

Develop scalable software applications and AI-driven solutions, research emerging AI tools, prototype innovations, and collaborate with stakeholders to improve workflows and products. The role involves applying machine learning concepts, LLMs, generative AI frameworks, data preprocessing, feature engineering, and model evaluation while promoting AI knowledge sharing across teams.

Manage and grow Optibus’ Brazil customer base by leading complex B2B and B2G sales cycles, partnerships, RFPs, negotiations, and deal closures. Build client relationships, manage pipelines, exceed revenue KPIs, maintain Salesforce forecasting data, collaborate with Marketing, Product, Customer Success, and Pre-Sales teams, and represent the company at industry events. The role requires English and Portuguese fluency and national and international travel.

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.

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.

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.

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 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.

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.

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.

Design, build, and deploy ML models and end-to-end pipelines; collaborate with data and MLOps teams to integrate, monitor, retrain, and optimize models; implement cloud-based scalable solutions and document processes.

Build, deploy, and operate production AI agents and features on large Databricks datasets. Collaborate with product, support, and engineering stakeholders to define requirements, ship iteratively, and own monitoring, evaluations, costs, and failure handling. The role requires strong Python, machine learning, LLM API, MCP, cloud, and batch or streaming pipeline experience, with a focus on agentic engineering, testing, and production reliability.

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.

Develop, debug, test, and optimize machine learning models, training pipelines, inference systems, numerical methods, and supporting infrastructure. Build reproducible Python workflows, diagnose computational issues, validate implementations, optimize memory and throughput, use coding agents, and document technical findings. The role is remote worldwide and requires approximately 15 hours per week with rapid task availability after onboarding.

Design, build, deploy, and operate production-ready AI and machine learning systems for enterprise customers. Translate architectures and requirements into tested code, data and model pipelines, APIs, and AI applications. Implement predictive ML, generative AI, LLM, agentic, and intelligent automation solutions with evaluation, monitoring, guardrails, security, and governance. Integrate solutions with enterprise platforms and contribute reusable accelerators, engineering standards, and technical playbooks while collaborating with architects, data scientists, engineers, stakeholders, and clients.

Own model strategy and evaluation for a production agentic AI platform. Select and benchmark LLMs, design statistically rigorous evaluation methods, build classifiers and fine-tuned models, engineer prompts and retrieval, diagnose model behavior, and ship reliable agentic products. The role includes production Python development, quality gates, observability, integrations with Databricks and external systems, and collaboration across engineering, data, and product teams.

Design, deploy, monitor, and optimize scalable MLOps pipelines and agentic AI systems in production. Manage cloud infrastructure, CI/CD and continuous training workflows, model monitoring, security, governance, compliance, and performance optimization. Collaborate with researchers, data scientists, engineers, and product teams to deliver reliable, ethical autonomous AI solutions.

Build and deploy production-grade AI and LLM systems for client engagements, including structured document extraction, RAG, agents, evaluations, and governed data foundations. Develop tested Python services, CI/CD pipelines, cloud deployments, and cost- and latency-aware architectures. Work directly with clients through discovery, demos, prototyping, presales, and delivery while addressing security, privacy, quality, and business-impact concerns.

Build scalable Python backend APIs, ML platforms, MLOps pipelines, and production LLM systems. Develop RAG and Generative AI workflows, model-serving infrastructure, feature pipelines, observability, prompt management, and multi-tenant architectures. Integrate Azure OpenAI, vector databases, OCR services, and enterprise systems while optimizing performance, reliability, security, and cost. Write maintainable, testable code; troubleshoot distributed applications; participate in design reviews and agile ceremonies; and collaborate with client-facing teams on technical requirements.

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.