Top Machine Learning Jobs in Fortaleza

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.

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.

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.

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.

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.

Design, deploy, and optimize large-scale recommender systems and machine learning pipelines. Build recommendation models using architectures such as Wide & Deep, Two-Tower, Transformer, embedding-based, autoencoder, and sequential models. Process data with Databricks and Spark, integrate models into production, monitor performance, conduct experimentation and A/B testing, and collaborate with engineering, data science, and business teams.

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.

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.

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.

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.

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

Design and scale agentic multi-agent systems that connect LLMs to internal APIs and tooling. Build production Python microservices, implement RAG, fine-tuning, and prompt engineering, and develop evaluation pipelines for LLM agents. Integrate vector databases and MCP, collaborate with product and conversation design, and ensure responsible, compliant AI-powered customer support experiences at scale.

As a Principal Technical Recruiter, you'll lead high-level searches while integrating AI into recruiting workflows, manage client relationships, and coach on using the AI recruiting platform, Moon Engine.