At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.

With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.

We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.

• Own a portfolio of forked backend services (mostly Java/Spring Boot): bug fixes, small changes, and day-to-day stability.
• Drive knowledge transfer with the original team through shadowing sessions. Ask the right questions, map dependencies, and turn tacit knowledge into something written.
• Build service context documentation in three layers (business rules, application behavior, operations) following the client's template. This feeds both the human team and the AI-assisted development model the client already runs in production.
• Support incidents in a production environment that runs 24/7 across global markets, including critical flows like orders and payments.
• Work within an Azure-based infrastructure (Azure DevOps, pipelines, and subscriptions), contributing to the duplication and adaptation of CI/CD pipelines for the new environment
• Participate in the team's shift toward better engineering practices: adopting GitFlow, strengthening test coverage, and gradually incorporating quality gates — without breaking what already works
• Help classify services by technical criticality and flag systemic risks early, such as hidden dependencies, shared databases/queues, and fragile integrations.

• Hands-on use of AI-assisted development tools (coding agents, assistants) in real work, with judgment about when to trust and when to verify.
• Java (backend, production experience) — this is non-negotiable
• Spring Boot — the core framework across the service landscape
• REST API design and integration
• Azure DevOps — familiarity with pipelines, repos, and environments
• Non relational (CosmosDB for MongoDB)  and relational databases (PostgreSQL)
• Testing — JUnit for unit tests; familiarity with E2E or integration testing
• Incident response mindset — ability to diagnose, triage, and resolve issues in complex distributed systems under pressure
• English — for reading documentation, participating in cross-team calls, and written communication
• Hands-on experience operating and debugging distributed microservices in production: logs, tracing, triage under pressure.
• Clear, proactive communication. You'll extract knowledge from a team that is moving on, so you must be able to drive conversations, write things down, and escalate early.
• Comfort working on inherited/legacy code with a "understand first, change carefully" mindset.

• Experience with microservices at scale — especially the operational realities of running and debugging many services in production
• Azure cloud services beyond DevOps (Service Bus, Blob Storage, Functions, etc.)
• Familiarity with DDD (Domain-Driven Design) concepts and service boundary thinking
• Go — a secondary language used in some newer services
• Understanding of internationalization and global commerce (multi-currency, multi-region systems)
• Performance optimization experience in high-throughput environments

• Mid-Level: Delivers fixes and changes independently on well-understood services, with guidance on the more complex ones. Strong Java/Spring Boot fundamentals and solid microservices concepts. Takes part in incident response alongside senior engineers. Learns a complex domain fast through shadowing and documentation, and writes down what they learn.
• Senior: Delivers the hardest changes and fixes, especially on critical flows such as orders, pricing and payments. Leads incident resolution end to end, from diagnosis to fix in production. Drives knowledge transfer sessions with the outgoing team and turns what comes out of them into documentation and service context. Spots systemic risks such as hidden dependencies, shared resources and fragile integrations. Sets the quality bar for the team through code reviews, tests and standards, mostly by example.
  
  
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