Engenheiro de IA SR
New
J
JobgetherHealthcare Technology
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonGCPMachine LearningBigQueryLLMGenerative AILangChain
Requirements
- Solid professional experience with Python development in production environments.
- Proven experience building, deploying, and managing generative AI solutions using LLM APIs.
- Strong knowledge of AI agent orchestration frameworks such as LangGraph, LangChain, CrewAI, or equivalent technologies.
- Experience with applied machine learning, including predictive modeling, validation, tuning, and deployment processes.
- Experience working with data lakehouse architectures such as Delta Lake, Iceberg, or similar technologies.
- Hands-on experience with cloud platforms, preferably Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, GCS).
- Knowledge of AI evaluation methodologies, RAG systems, LLM observability, or Model Context Protocol (MCP).
- Experience with workflow orchestration tools such as Airflow, Kestra, Dagster, or similar.
- Previous exposure to healthcare technology, clinical data, billing systems, or healthcare interoperability is an advantage.
- Strong ownership mindset and pragmatic problem-solving skills.
Responsibilities
- Build and manage production-grade generative AI agents, including multi-agent architectures, orchestration workflows, tool calling, guardrails, and human-in-the-loop processes.
- Develop machine learning models for predictive applications, covering feature engineering, validation, deployment, and production monitoring.
- Design and evolve data pipelines based on lakehouse architectures to support AI models and intelligent systems.
- Implement systematic evaluation processes for GenAI solutions, including quality metrics, performance analysis, and cost optimization.
- Translate complex healthcare-related challenges into robust, observable, secure, and sustainable AI solutions while ensuring compliance with privacy requirements.
- Take ownership of solutions throughout their lifecycle, from initial concept and architecture decisions to deployment and continuous optimization.
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