Senior AI Engineer
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English proficiency
- Required Skills
- PythonSQLAgileGitSparkRESTful APIsDatabricks
Requirements
- Advanced English proficiency.
- Strong experience developing AI applications using Python, SQL, PowerShell, REST APIs, and structured data formats such as JSON.
- Experience designing and implementing LLM-powered and agentic applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, embeddings, context management, tool calling, and workflow evaluation.
- Hands-on experience with Microsoft Azure AI ecosystems, including Azure AI Services, Azure OpenAI-compatible services, Azure API Management, Cosmos DB, and Microsoft Fabric.
- Strong knowledge of cloud-native architectures and scalable AI application development within enterprise environments.
- Experience with AI engineering practices focused on production readiness, reliability, testing, and maintainability.
- Experience working with Databricks, Apache Spark, and PySpark to support AI, analytics, and data engineering workflows.
- Proficiency with Git, GitHub, and Azure DevOps for version control, collaboration, and CI/CD practices.
- Ability to use AI-assisted development tools and workflows effectively to accelerate implementation, improve quality, create documentation, develop tests, and troubleshoot solutions while maintaining technical accountability.
- Strong problem-solving skills, collaboration abilities, and interest in continuous learning.
- Experience working in Agile environments and consulting contexts is a plus.
Responsibilities
- Lead complex AI engineering workstreams across multiple business areas, use cases, products, and stakeholder groups.
- Define AI engineering strategies that align business objectives, workflow opportunities, orchestration approaches, integration requirements, and measurable outcomes.
- Translate complex and ambiguous requirements into structured implementation plans, including workflow logic, prompt orchestration, retrieval strategies, tool-calling approaches, and solution recommendations.
- Design, build, and improve AI-powered applications, agents, assistants, and workflow components that connect AI models with practical business processes.
- Establish and promote best practices for prompt engineering, context management, retrieval flows, API integrations, testing, response processing, and code quality.
- Develop reusable AI implementation patterns for use cases such as summarization, extraction, search augmentation, question answering, conversational experiences, automation, and agent-assisted workflows.
- Contribute to delivery planning, prioritization, technical standards, and quality assurance across AI engineering initiatives.
- Collaborate with Data Scientists, AI Specialists, Data Engineers, Analytics Engineers, and Architects to create solutions aligned with data governance, technical constraints, and organizational needs.
- Ensure AI solutions follow responsible AI principles, security standards, privacy requirements, and governance frameworks.
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