The role involves monitoring the health, performance, and reliability of AI/ML applications while managing logging, alerting, and cost tracking. You will collaborate with engineering teams to troubleshoot issues and support AI quality evaluations.
The engineer will create enterprise knowledge foundations using ingestion pipelines, vector databases, and knowledge graphs. They are responsible for managing document ingestion pipelines and overseeing the knowledge graph platform.
The developer will build the AI Development Lifecycle (AIDLC) and associated evaluation frameworks. They are responsible for creating agent harnesses, templates, governance pipelines, and developer experience tooling.
The role involves converting Transmission and Distribution network models from CYME and GE PSLF into pandapower-based Python models. The engineer will collaborate with NVIDIA to develop and benchmark GPU-accelerated powerflow analysis solutions using CUDA libraries.
