Sr AI Engineer (ML & Data)
New
J
JobgetherFleet compliance
Based in Brazil; flexibility to work from anywhere in Latin America, Availability to overlap with US Eastern business hours, approximately 9 AM–5 PM.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced written and spoken English
- Required Skills
- AWSPythonMachine LearningAzureDatabricks
Requirements
- Professional background in machine learning or AI engineering.
- Proven experience delivering AI agents or LLM-powered features to production with real users relying on them.
- Hands-on Databricks experience, including developing and managing batch and streaming data pipelines.
- Cloud experience with AWS and/or Azure.
- Experience using evaluations and data to assess agent deployment readiness and measure production effectiveness.
- Strong Python proficiency and practical experience building solutions with LLM APIs and MCP.
- Familiarity with agent frameworks or SDKs, including knowing when a framework is unnecessary.
- Established experience using coding agents as the primary approach to software development.
- Ability to work independently and gather requirements from non-engineering stakeholders.
- Advanced written and spoken English.
- Availability to overlap with US Eastern business hours, approximately 9 AM–5 PM.
- Willingness to participate in hands-on technical interviews and discuss previously built and shipped solutions.
Responsibilities
- Design, build, and deploy AI agents and features from initial specifications through production.
- Deliver early iterations and improve solutions using feedback from real users.
- Translate business challenges into technical requirements with product, support, and engineering stakeholders.
- Develop AI agents and machine learning features using large datasets and Databricks.
- Build and manage batch and streaming data pipelines for AI-powered applications.
- Monitor production solutions, evaluate agent performance, manage costs, and troubleshoot failures.
- Promote agentic engineering best practices to improve development speed, reliability, and team productivity.
- Use coding agents to define tasks, review generated outputs, validate results, and coordinate parallel workflows.
- Strengthen engineering processes through testing, CI, agent reviews, and reusable skills.
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