Top Machine Learning Jobs in Fortaleza

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.

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.

Designs, builds, and deploys production AI solutions using machine learning, deep learning, generative AI, and LLMs. Responsibilities include model development and evaluation, RAG pipelines, NLP applications, AI service integration, MLOps, data preparation, deployment, monitoring, testing, and scalable software engineering. Collaborates with engineering, product, and data teams in a distributed environment while supporting responsible, performant, and cost-effective AI development.

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.

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.

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.

Owns financial data and BI product strategy across Quote-to-Cash and Order-to-Cash processes. The role identifies operational gaps, translates finance and revenue lifecycle needs into PRDs and roadmaps, validates pricing and contract data, and develops dashboards and AI/ML analytics opportunities. It partners with Sales, Legal, Accounting, Finance, Product, and data engineering teams to improve downstream reporting and decision-making.

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.

Lead enterprise delivery of Databricks-based data, analytics, and Generative AI solutions. Design production data pipelines, lakehouse architectures, RAG applications, ML platforms, and cloud-native systems. Drive legacy data migrations to Databricks, advise executive client stakeholders, identify platform expansion opportunities, and mentor engineering and consulting teams. The role combines hands-on engineering, solution architecture, customer engagement, and technical practice development.

Lead the full product lifecycle by owning roadmaps, translating business priorities into executable plans, defining features with engineering and UX/UI teams, and driving timely delivery. Conduct user research, establish product metrics, lead client demos, support go-to-market positioning, and align product development with broader innovation goals. Manage cross-functional teams, including ML engineers, while iterating products based on user feedback.

Design, build, deploy, and maintain scalable production machine learning systems. Responsibilities include developing end-to-end ML pipelines, distributed data workflows with PySpark and SQL, model deployment across AWS, Azure, GCP, and Databricks, and implementing MLOps practices such as versioning, monitoring, CI/CD, and lifecycle management. The role also manages Kubernetes-based workloads, optimizes model performance and reliability, and collaborates with data, product, and business teams.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.