Sr. Machine Learning Engineer
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El puesto
Design and implement end-to-end agentic workflows where AI agents collaborate to solve multi-step business problems.
Architect robust agent orchestration layers, manage state, memory, and sequential handoffs across agents.
Hybrid agent strategy: seamlessly integrate out-of-the-box agents with highly specialized custom agents built on proprietary data.
Develop glass-box observability with deep tracing and logging to visualize the chain of thought and why decisions were made.
Build core modeling capabilities (regression, classification, forecasting, clustering) and perform feature engineering and statistical validation to ensure reliability.
Deploy in production via Docker and Kubernetes, build APIs (FastAPI/Flask), implement CI/CD, monitor drift, manage latency/cost, and evangelize AI capabilities to stakeholders.
Architect robust agent orchestration layers, manage state, memory, and sequential handoffs across agents.
Hybrid agent strategy: seamlessly integrate out-of-the-box agents with highly specialized custom agents built on proprietary data.
Develop glass-box observability with deep tracing and logging to visualize the chain of thought and why decisions were made.
Build core modeling capabilities (regression, classification, forecasting, clustering) and perform feature engineering and statistical validation to ensure reliability.
Deploy in production via Docker and Kubernetes, build APIs (FastAPI/Flask), implement CI/CD, monitor drift, manage latency/cost, and evangelize AI capabilities to stakeholders.
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