CI&T

Software Engineer

• Participate in legacy discovery: understand flows, map business rules and document components (programs, copybooks, JCLs, DB2 tables, VSAM files and CICS transactions).
• Perform impact and dependency analysis to support the migration/modernization plan.
• Implement adjustments in the legacy system for stabilization and decoupling, when necessary, enabling phased migration.
• Execute and support regression testing, debugging and fixes (online and batch), ensuring stability during the transition.
• Support transition strategies such as parallel runs (dual-run), result validation/reconciliation and cutover preparation.
• Lead decommissioning activities: removal of jobs, routines, transactions, datasets and integrations, ensuring evidence, traceability and risk mitigation.
• Produce and maintain relevant technical and functional documentation for audit, support and knowledge transfer.

Senior Developer, Mainframe/COBOL

Analyst

• Develop and maintain test automation frameworks for web applications, following software development best practices.
• Create and execute automated tests, ensuring coverage of critical features and relevant user scenarios.
• Analyze automated test results, identify and report bugs clearly and accurately using tracking tools.
• Actively participate across the software development lifecycle, from planning to release, contributing to the definition of the test strategy.

Senior Quality Automation Analyst

Data Engineer

• Design, develop, and maintain data pipelines (batch and streaming) for ingestion, transformation, and delivery of data for analytics and application consumption.
• Build and evolve analytical modeling (bronze/silver/gold layers, data marts, star schemas, wide tables), ensuring consistency, documentation, and reusability.
• Implement data quality best practices (tests, validations, contracts, SLAs/SLOs, monitoring of freshness/completeness/accuracy) and manage incident resolution with root cause analysis (RCA).
• Define and maintain technical data governance: catalog, lineage, versioning, naming conventions, ownership, access policies, and audit trails.
• Optimize performance and cost of queries and pipelines (partitioning, clustering, incremental loads, materializations, job tuning).
• Support the full delivery lifecycle (discovery → development → validation → operations), aligning business requirements with technical needs and ensuring predictability.
• Collaborate with BI/Analytics teams to define metrics, dimensions, facts, and the semantic layer, ensuring traceability of key indicators.
• Enable and operationalize AI/ML use cases.
• Integrate sources and systems (APIs, databases, queues, events, files), ensuring security, idempotency, fault tolerance, and end-to-end traceability.
• Produce and maintain technical and functional documentation relevant for auditing, support, and knowledge transfer.

Senior Data Engineer, Analytics

Full-stack Engineer

• Contribute to the evolution of a modernization workflow supported by Artificial Intelligence.
• Explore, test, and apply AI tools and models in the context of software development.
• Use and enhance development support tools and practices, with special focus on GitHub Copilot.
• Identify technical gaps during modernization processes and propose improvements.
• Support other teams in understanding and using the modernization framework developed by the team.
• Work collaboratively with senior developers and architects.
• Plan and manage your own activities, ensuring organization, meeting deadlines, and clarity of priorities.
• Report status, progress, and impediments clearly and objectively.
• Share technical knowledge and learnings with the team.

Mid Level Full Stack Developer, Modernization, AI-Driven

• Design, develop, and maintain scalable, robust data pipelines.
• Build data ingestion, transformation, and modeling solutions using Databricks, Spark/PySpark, Cloudera, and Azure Data Factory (ADF).
• Ensure data quality, integrity, and usability across the entire pipeline.
• Design and maintain Data Lake and Data Warehouse solutions, applying data governance best practices.
• Work with concepts and frameworks such as Bronze, Silver, and Gold layers, Star Schema, Delta Tables, Delta Sharing, and analytical tables.
• Collaborate with technical and business teams to orchestrate solutions aligned with company objectives.

Mid Data Developer – Cloudera

DevOps Engineer

• Develop and evolve an internal development platform (IDP) focused on cloud provisioning, automation, and standardization.
• Implement and maintain robust automation, CI/CD, observability, and security practices at the infrastructure level.
• Collaborate with development, security, and SRE teams to promote a strong platform engineering culture.
• Ensure the platform is scalable, resilient, secure, and user-friendly, supporting multiple teams and use cases.
• Design architectures, patterns, tools, and workflows that increase teams' efficiency and delivery velocity.

Senior DevOps

• Work on-site and serve in a consultative capacity with operations teams (Cajamar and Vila Olímpia regions) to gain a deep understanding of users' routines and pain points.
• Analyze current "AS-IS" processes, identifying bottlenecks, overly verbose procedures, and inefficiencies in the use of existing systems.
• Translate operational needs into business requirements for the application of Artificial Intelligence and Intelligent Agents, defining what needs to be done to solve problems and proposing solutions that leverage AI agents, Machine Learning, and data.
• Act as a bridge between operations and the technical team, ensuring that proposed solutions (especially AI-based ones) are aligned with real process constraints and deliver tangible value.
• Propose the design of new "TO-BE" workflows empowered by technology, focusing on simplifying and streamlining operational tasks.

Senior AI Developer, GCP

Backend Engineer

• Understand customer business problems and pain points;
• Write high-quality software code;
• Perform code reviews;
• Help to solve business problems;
• Work with multidisciplinary teams following an agile delivery model;
• Communicate in English, actively participating in meetings and reviewing and writing technical documents.

Mid-level Java Developer

Engineer

• Validate application quality.
• Demonstrate ownership and accountability for work assignments; be resourceful and collaborate with cross-functional teams to quickly resolve performance and scalability challenges.
• Design workloads and simulate usage patterns to model application behavior and establish performance and scalability benchmarks.
• Measure system performance metrics, including throughput, response times, and resource usage such as CPU, memory, thread utilization, and I/O statistics.
• Work with messaging queues and caching technologies.
• Automate tracking and publishing of performance results across software builds and releases to ensure high-quality deployments.
• Perform root cause analysis for performance issues and architecture optimizations; dive deep into problem areas and contribute fixes that are faster, cleaner, and easier to maintain.
• Develop and maintain test scripts using JMeter or other open-source tools.
• Ensure efficient resource utilization after deployment in the cloud.
• Monitor hosts and applications in the cloud for resource contention.
• Estimate performance testing deliverables for applicable projects.
• Gather requirements, create JMeter scripts, and execute tests.
• Monitor build jobs and create defects for any performance-related issues.
• Create GitHub (or Git) pipelines for performance test automation.
• Script new functionality and fix existing or failing JMeter and Gatling scripts.
• Plan and execute performance tests in QA and pre-production environments (and occasionally production).
• Collaborate with teams on new services, gather requirements, and develop JMeter/Gatling scripts.
• Develop, document, and maintain test strategies and test scenarios.
• Execute tests using cloud-based load generators according to requirements.
• Collaborate with Operations teams to monitor applications and ensure they perform as expected.
• Perform detailed analysis of any bottlenecks found during test execution.
• Build enterprise-level stress, load, capacity, and soak tests.
• Communicate application performance metrics to leadership.
• Develop and maintain Key Performance Indicators (KPIs) for applications.
• Develop and maintain service-level metrics, including response time, failover, time to failure, and time to recover.
• Coordinate with team leads and development managers.
• Present KPIs and performance metrics to leadership in a timely manner.

Performance Engineer

QA Engineer

• Participate in developing and documenting User Stories by providing the test strategy and automation estimates;
• Develop, document, and maintain the test strategy and test scenarios;
• Build and execute unit, component, acceptance, integration, load, security, scalability, usability, and platform tests;
• Conduct code reviews to ensure consistency, quality, and reusability of test automation artifacts;
• Partner with developers to ensure all areas of the application are fully tested and to find technical solutions for test automation;
• Build and manage automated test suites and ensure execution across various environments, CI/CD pipelines, and for use by NOC teams;
• Regularly execute, maintain, and update automated test suites and analyze results to identify causes of failures;
• Create defects for issues found with appropriate details and ensure issues are resolved, including validation of fixes and required automation updates;
• Present appropriate results to approve software functionality for new Production releases;
• Work with cross-team and release management to coordinate dev, QA, and production releases of the software;
• Perform exploratory testing to understand features and implementation, discover additional or unexpected behavior, and refine or define additional test cases for the feature;
• Contribute to the QA automation framework using industry-standard tools and approaches;
• Leverage and enhance coding standards and best practices for automated QA code;
• Participate in analyzing Major Incidents and Emergency Change controls to identify opportunities to automate software quality test cases to reduce or eliminate those incidents;

Senior Software QA Automation

• Manage and optimize our AWS cloud infrastructure to ensure reliability, scalability, and security.
• Implement and maintain IaC practices
• Leverage AI tools and technologies to automate and enhance daily operations and workflows.
• Work closely with development and operations teams to streamline processes and improve deployment practices.
• Monitor system performance, identify areas for improvement, and implement best practices for continuous delivery and integration.
• Develop and maintain Tableau dashboards to visualize metrics and provide insights for decision-making.

Mid-level DevOps, Tableau

• Uniting human expertise with AI to create scalable technology solutions
• Maintaining a clean-code mindset
• Collaborating within a tech transformation team
• Developing features with full test coverage

Senior Full Stack Developer, .NET/Angular

Cloud Engineer

• Desenvolver e manter um aplicativo core que suporte outros sistemas internos.
• Desenvolver e manter telas de um aplicativo web.
• Colaborar com equipes multidisciplinares para garantir a integração eficiente entre sistemas.
• Melhorar a estrutura da aplicação para garantir escalabilidade horizontal na infraestrutura AWS.
• Trabalhar com monolitos existentes (Python, Java e Node) e participar da estratégia de evolução desses sistemas.
• Promover uma cultura de testes na equipe, dado que não há QAs dedicados.

Senior Java, ReactJS, AWS Developer

• Serve a financial services client alongside CI&T.
• Provide support for the team's technical leadership.
• Perform agile, high-quality deployments.

Senior Java/React Fullstack

AI Engineer

• Design, develop, and deploy AI-driven applications and services, with a strong focus on Machine Learning and Generative AI use cases.
• Build and integrate LLM-based solutions using frameworks such as LangChain, ensuring reliability, scalability, and performance.
• Collaborate with product managers, designers, and data teams to translate business requirements into technical AI solutions.
• Implement data pipelines, model training workflows, and inference services across different environments.
• Optimize models and systems for performance, cost, and scalability in production.
• Ensure best practices related to model evaluation, monitoring, versioning, and responsible AI usage.
• Contribute to technical discussions, architectural decisions, and proof-of-concepts for AI initiatives.
• Stay up to date with emerging AI technologies, tools, and industry trends.