Tasks:
- * Apply observability and security practices
- * Automate model lifecycles with CI/CD
- * Build data and model training pipelines
- * Build RAG and agent orchestration systems
- * Deploy and monitor models in production
- * Develop and test machine learning and AI models
- * Develop inference APIs and services
- * Implement Generative AI and Agentic AI solutions
- * Implement MLOps and LLMOps practices
- * Implement responsible AI practices
- * Integrate models with business applications
- * Monitor solution performance, quality, and availability
- * Operationalize AI solutions
- * Optimize vector database and semantic search solutions
Perks/Benefits:
Skills/Tech stack required:
[Agentic AI] [AI Foundry] [AI Search] [APIs] [Azure AI] [Azure AI Foundry] [Azure AI Search] [Azure Databricks] [CI/CD] [Data Pipelines] [Deep learning] [DevOps] [Docker] [English] [Generative AI] [Git] [Langchain] [Langgraph] [LLMOps] [Machine Learning] [Microsoft Azure] [MLOps] [Model Deployment] [Portuguese] [Python] [RAG] [Semantic Kernel] [Semantic Search] [SQL] [Vector Databases]
Educational requirements:
[Bachelor's degree in Computer Engineering, Data Science, Artificial Intelligence, Mathematics, Statistics, or related field] [Master's degree in Computer Engineering, Data Science, Artificial Intelligence, Mathematics, Statistics, or related field]
Role(s):
[AI/ML Engineer] [Artificial Intelligence] [Artificial Intelligence Engineer] [Engineer] [Intelligence Engineer] [Learning Engineer] [Machine Learning Engineer] [ML Engineer]