Top Machine Learning Jobs in Fortaleza

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.

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.

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.

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.

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.

Conduct exploratory analysis, statistical modeling, experimentation, forecasting, and machine learning for marketplace and pricing systems. Analyze supply, demand, elasticity, conversion, and pricing behavior; build predictive models; improve data quality and analytical foundations; contribute to production ML; and communicate actionable recommendations to Product, Engineering, Pricing, and business stakeholders.

Leads consultative discovery, technical qualification, solution design, executive engagement, sales campaigns, and customer presentations for AspenTech solutions across Metals & Mining, LNG, Specialty Chemicals, and Polymer industries in LATAM. Translates industry challenges into measurable business value, develops industry-specific messaging, supports sales teams, informs product management, and trains account managers and solution consultants. Requires extensive industry software-selling experience and travel up to 35%.

Supports the Solutions Engineering team by creating SQL demonstrations, exploratory analyses, queries, reports, visualizations, prototypes, and proof-of-concepts using public and customer datasets. Assists with environment setup, task automation, cloud infrastructure, and technical community initiatives such as events, webinars, training, and content creation. Collaborates with technical leadership while gaining practical experience in data science and analytics.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

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%.

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.

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.

Support the full data workflow: ingest and structure data, perform cleaning and validation, run exploratory analysis and visualizations, assist with ML/DL experiments and model evaluation, and contribute to predictive maintenance and ERCOT market analytics projects.

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.

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.

Design and deploy a multi-agent orchestration layer for conversational AI, build production-grade Python ML services, lead end-to-end ML projects, establish team best practices, mentor engineers, and ensure secure, scalable, high-performance customer-facing AI systems.

Design, build, and deploy agentic LLM workflows that plan, call tools, maintain state, self-correct, and integrate with external systems. Implement monitoring, evaluation, guardrails, error handling, and human-in-the-loop checkpoints for safe, long-running multi-step agent executions.

Lead the architecture and development of scalable AI/ML pipelines and production systems, including LLMs and Generative AI. Mentor engineering teams, guide technical decisions, operationalize models, optimize performance and costs, and implement MLOps practices such as CI/CD, monitoring, versioning, and maintenance. Partner with executives, clients, product managers, data scientists, and software engineers to align AI roadmaps with business goals and integrate ethical AI capabilities into enterprise applications.

Responsible for managing a team of cloud engineers, overseeing technical tasks, engaging with clients, mentoring team members, and ensuring technical accountability on projects.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.