[Job-29481] Senior AI Developer (Customer Delivery), Brazil

Na CI&T , ajudamos grandes empresas a transformar o potencial da AI em impacto real nos negócios com AI Deployment, execução AI-native e tech-integrated business solutions. Com 30 anos de experiência em transformação tecnológica, aceleramos inovação com expertise em agentic SDLC, application modernization, Data & AI, martech e business strategy.  Somos 8.000 CI&Ters em mais de 25 países, colaborando para construir soluções com impacto real. AI já faz parte da forma como trabalhamos, evoluímos e inovamos todos os dias. Importante: se você reside na Região Metropolitana de Campinas, sua presença nos escritórios da cidade será obrigatória, conforme a política de frequencia vigente. We are looking for a Senior Generative AI Engineer to join the Customer Delivery team at CI&T. This person will work embedded within strategic client projects, helping companies across industries such as banking, insurance, and retail successfully adopt and operationalize Generative AI solutions in real production environments. The challenge involves building and scaling AI-powered solutions such as RAG pipelines, AI agents, and intelligent automations while enabling client teams to operate autonomously and evolve their engineering capabilities. We are looking for someone hands-on, highly technical, and passionate about applying AI to solve real business and engineering problems at scale.ResponsibilitiesBuild and operationalize Generative AI solutions within client projects, ensuring value delivery from the beginning of the engagement. Develop and evolve RAG pipelines, AI agents, intelligent automations, and integrations with LLMs in production environments. Design and implement AI-assisted engineering solutions to improve software development lifecycle efficiency and quality. Support technical discovery, architecture definition, and technical decision-making across projects. Enable and mentor client engineering teams, accelerating adoption of AI solutions and modern engineering practices. Collaborate with engagement managers, technical teams, and stakeholders to ensure alignment between technical implementation and business goals. Evaluate technical contexts, constraints, and existing architectures at the beginning of engagements. Build proof of concepts (POCs) and validate technical approaches before scaling implementations. Contribute to software architecture, APIs, integrations, distributed systems, and cloud-native environments. Create and maintain clear technical documentation and operational guidelines.Requirements for this challengeExperience in software engineering and modern distributed systems. Hands-on experience with Generative AI solutions in production environments. Experience building RAG pipelines, embeddings, vector databases, and prompt engineering solutions. Experience integrating and consuming LLM APIs such as OpenAI, Claude, Gemini, or similar. Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar solutions. Experience building or integrating AI agents and AI-assisted development workflows. Knowledge of backend development using Node.js, Python, Java, .NET, or similar languages. Knowledge of APIs, REST services, contracts, and versioning. Experience with distributed systems, microservices, and event-driven architectures. Experience with relational and non-relational databases. Experience with cloud platforms such as Azure, AWS, or GCP, including AI services like Azure OpenAI, AWS Bedrock, or Vertex AI. Knowledge of DevOps practices, CI/CD, testing, and automation. Experience with unit, integration, and end-to-end testing. Ability to work across multiple client engagements and contexts simultaneously. Advanced English communication skills. Strong communication, collaboration, and technical influence skills.DifferentialsExperience with multi-agent orchestration frameworks such as LangGraph, CrewAI, AutoGen, or similar. Experience with AI-assisted modernization and legacy migration strategies. Knowledge of Spec-Driven Development, Contract-First, or similar engineering approaches. Experience with Kubernetes, containers, and infrastructure automation. Knowledge of observability, security, and performance for AI systems in production. Experience working in consulting environments or embedded client delivery models. Experience enabling teams and scaling AI adoption across engineering organizations.