Senior AI Engineer
New
J
JeevesFinancial technology
This role is remote or hybrid based out of São Paulo, Brazil, and is a full-time remote position where it is also possible to come into our office at complexo JK Iguatemi on a flexible schedule.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Professional fluency in English, written and spoken
- Experience
- 7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
- Required Skills
- PostgreSQLPythonCloud ComputingRESTful APIs
Requirements
- 7+ years of professional software engineering experience.
- At least 2 years building and operating LLM or AI-powered systems in production.
- Experience owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction.
- Hands-on experience shipping LLM-powered applications using APIs such as Anthropic, OpenAI, or similar.
- Experience with structured outputs, error handling, and evaluation for LLM-powered applications.
- Experience designing agentic or multi-step AI workflows, including tool use, orchestration, and human review steps.
- Strong proficiency in Python.
- Backend fundamentals including REST APIs, PostgreSQL or similar relational databases, and async patterns.
- Experience with a major cloud provider: AWS, GCP, or Azure.
- Experience with AI system observability, including logging, tracing, dashboards, and quality monitoring.
- Professional fluency in written and spoken English.
Responsibilities
- Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use, with human-in-the-loop points.
- Write design documents, make build-versus-buy and model choices, and set workflow success metrics.
- Build production LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring.
- Integrate AI services with backend systems using clear API contracts, retries, graceful degradation, and per-customer data isolation.
- Manage model costs and latency by selecting appropriate models for each workflow step.
- Build evaluation sets and automated evaluations, and use them to catch regressions when prompts, models, or data change.
- Instrument AI components with logging, tracing, dashboards, and alerts, and maintain an audit trail of AI decisions.
- Set shared AI engineering patterns, tooling, and practices; help engineers use coding agents and AI tools; and review peers' AI system designs.
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