Senior - AI Deployment Engineer, Brazil

Take the solution to production and keep it running reliably and securely, at the speed AI demands, covering both the deployment of conventional applications (app stores, CDN, mobile release trains, feature flags and blue/green service rollouts) and that of AI systems. Designs and manages the deployment corridor and the CI/CD pipelines for agentic environments, provisioning infrastructure, defining the release cadence, security and compliance pipeline gates and rollback strategies for fast recovery. This is the technical evolution of DevOps and SRE into the world of models, what the market calls LLMOps: serving, managing the observability platform and the health metrics of the solution, AI FinOps with active management of the inference budget and intelligent model routing, perimeter security, infrastructure guardrails and management of package versioning, environment configurations and model endpoints in production. Closes the downstream by ensuring that what was built reaches the client stable, secure and economically viable, and feeds the delivery line back with operational signals for continuous optimization of delivery.

Responsibilities

• - Take the solution to production, designing CI/CD pipelines for agentic environments and reducing the time between building and delivering (lead time).
• - Execute the deployment of conventional applications (app stores, CDN, mobile release trains, feature flags and blue/green service rollouts), to bring any product to the client safely.
• - Provision and automate the standardized configuration of the dev, staging and production environments.
• - Operate the serving of models and agents, ensuring availability, latency and scale in production.
• - Establish quality, security and compliance pipeline gates at every stage of the delivery line before release.
• - Execute versioning strategies, manage the release cadence and run rollback and fast recovery procedures in case of failure.
• - Instrument observability and metrics over the AI solution, monitoring behavior, quality and token cost. Manage the AI FinOps of the operation, optimizing the inference budget and the intelligent routing of models (smaller ones for simple tasks, larger ones only when necessary), to improve the cost-performance ratio in production.
• - Ensure the security of the solution in production, with access control, data protection and mitigation of AI risks, to operate within the client's compliance boundaries.
• - Apply infrastructure guardrails that contain model risk and improper agent behavior in production.
• - Version application packages, environment configurations and model endpoints, ensuring traceability and safe rollback.
• - Feed upstream and the AI Engineer back with production signals on cost, failures and real performance.
• - Apply ethics, privacy, data security and compliance practices, to keep the solutions within the client's and CI&T's rules.

Requirements for this challenge

• LLMOps
• DevOps Engineering
• Site Reliability (SRE)
• AI Security and Access Control
• Observability and Monitoring
• AI FinOps and Cost Optimization
• Guardrails and AI Safety
• CI and CD
• Model Routing and Selection

Nice to Have

• Cloudcomputingconcepts

• AppliedInfrastructureasCode(Terraform)

• KnowledgeofAzureContainerApps

• Scalability

• Connectivity

• Codereview

• Kubernetes(K8s)containers