ND
Remote
(Anywhere)
Salary Range
BRL
Full Time Employee
Experience Level
Mid level
Requirements
3+ years of experience in the career
GitHub Actions
Kubernetes
TypeScript
Java
Docker
GitHub Copilot
Azure
LLMs
Python
MCP
Desired Skills
OWASP
Infraestrutura como Código
LangChain
API REST
CI/CD
GCP
Testes automatizados
OAuth2
RAG
Clean Code
JavaScript
OpenShift
Segurança de APIs
AWS
Semantic Kernel
Microsserviços
Engenharia de Prompt
Tasks and Responsibilities
Show originalAbout the Role
Opening for a Cloud Development professional with experience in enterprise solutions, GitHub Copilot, Generative AI, and integration via MCP. The hired individual will develop and evolve intelligent agents, APIs, and scalable cloud platforms, contributing to the modernization of the software engineering lifecycle and process automation.
Responsibilities
- Develop enterprise Cloud solutions and scalable cloud platforms.
- Integrate Generative Artificial Intelligence services into company systems.
- Implement intelligent agents using GitHub Copilot and MCP protocols.
- Develop and consume MCP servers for integration between AI agents and corporate systems.
- Integrate APIs, GitHub, databases, and corporate services.
- Build tools made available to AI agents.
- Accelerate the development cycle and automate software engineering processes.
Requirements
- Between 3 and 6 years of experience in software development.
- Proven experience in Cloud projects.
- Experience in DevOps environments and agile methodologies.
- Proficiency in Java, Python, and JavaScript/TypeScript.
- Development of REST APIs and system integration.
- Microservices architecture.
- Automated testing.
- Clean Code and development best practices.
- Docker and Kubernetes/OpenShift.
- GitHub Actions and CI/CD pipelines.
- Infrastructure as Code.
- OWASP, OAuth2, and API security.
- Practical experience with Azure (preferably), AWS, or GCP.
GitHub Copilot
- Continuous use of GitHub Copilot for development.
- Code generation and refactoring.
- Automated test generation.
- Utilization of Agent Mode features and AI-assisted development.
Generative Artificial Intelligence
- Practical experience with LLMs.
- Prompt Engineering.
- RAG (Retrieval-Augmented Generation).
- Consumption of AI APIs.
- Frameworks such as LangChain or Semantic Kernel.
MCP (Model Context Protocol)
- Integration between AI agents and corporate systems.
- Development or consumption of MCP servers.
- Integration of APIs, GitHub, databases, and corporate services.
- Construction of tools made available to AI agents.
Soft Skills
- Autonomy in developing medium-complexity solutions.
- Ability to propose technical improvements.
- Ease in working within multidisciplinary teams.
- Product mindset and focus on value generation.
- Ability to disseminate knowledge to teams.
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