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AI Engineer
Caixa Vida e PrevidênciaAI Engineer at Caixa Vida e Previdência focused on building and operating production AI systems. Work includes LLMs, RAG, and corporate system integration.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building production AI solutions, particularly with LLMs and RAG, while ensuring integration with corporate systems and adherence to governance and security best practices. Proficient in implementing scalable architectures and monitoring performance and cost in cloud environments.
Highest-signal resume keywords
Software EngineeringExperience With .NET / C#REST APIs And MicroservicesAzure AI / Azure OpenAIIntegration With LLM APIs
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
.NET / C#REST APIsMicroservicesDistributed ArchitectureNode.jsAzurePrompt EngineeringDocument Indexing PipelinesData Modeling For AICI/CD
Tools & Technologies
Azure App ServicesAzure FunctionsCosmos DBSQL ServerDockerKubernetesObservability Tools
Industry Keywords
AI SolutionsIntegration With Corporate SystemsPerformance MonitoringGovernanceSecurity Best Practices
Tech Stack
Tools & technologiesAzureDockerJavaScriptKubernetes.NETNode.jsSQL
About the role
Key responsibilities & impact- Design, build, and operate production AI solutions with a focus on LLMs, RAG, agents, and integration with corporate systems
- Develop AI solutions integrated with corporate systems
- Build pipelines using LLMs, RAG, and AI agents
- Implement APIs, microservices, and distributed architectures
- Design scalable and resilient solutions
- Integrate AI with databases and legacy systems
- Monitor performance, cost, and quality of production solutions
- Ensure governance, security, and engineering best practices
Requirements
What you’ll need- Software engineering
- Experience with .NET / C#
- REST APIs and microservices
- Distributed architecture
- Node.js (frontend or APIs)
- Azure (App Services, Functions, Storage)
- Azure AI / Azure OpenAI / Foundry
- Authentication (Entra ID, RBAC)
- Cloud deployment and operations
- Integration with LLM APIs
- Prompt engineering in production
- Understanding of limitations (hallucination, cost, latency)
- Evaluation of response quality
- Embeddings
- Vector databases (e.g., Azure AI Search)
- Document indexing pipelines
- Concepts of chunking, retrieval, and grounding
- Agent orchestration (multi-step, tools)
- Integration with corporate APIs
- Automations and workflows
- Cosmos DB and SQL Server
- Data modeling for AI
- Document ingestion pipelines
- Designing scalable solutions
- Integration with legacy systems
- Integration between APIs, AI, and data
- Observability (logs and metrics)
- Cost and performance monitoring
- CI/CD
- Docker/Kubernetes (desirable)
- Multi-agent experience
- Model evaluation frameworks
- Fine-tuning and embeddings tuning
- Multimodal AI
- Experience with real production projects using LLMs
Benefits
Comp & perks- Profit sharing
- Flexible hours
- Meal and food allowance
- Wellhub
- Transportation allowance
- Health insurance
- Dental plan
- Pharmacy assistance
- Childcare and nanny assistance
- Life insurance
- Travel insurance
- Pension plan
- Maternity kit
- Maternity leave
- Paternity leave