Own the full machine learning delivery lifecycle, including data discovery, modeling, deployment, and monitoring within client environments. Maintain a client-facing engineering presence to lead discovery, demos, and feedback loops with client technical teams.
You will build and maintain reusable ML libraries, simulation engines, and grid-data toolkits to support utility client engagements. You will also own the feedback loop with client pods to refine capabilities based on real-world field performance.
The engineer will own the full-stack development of client-facing demos, including chat surfaces, agent workspaces, and app builder tools. They are responsible for maintaining design systems, ensuring enterprise readiness, and debugging across the entire stack from frontend to infrastructure.
You will own the AI inference and runtime platform, managing the serving tier, Kubernetes infrastructure, and stateful data planes. You will also oversee the sandbox runtime, control-plane services, and ensure high-performance, scalable model deployment.
You will build and maintain backend services for an LLM gateway, including routing, rate limiting, and observability. You will also contribute to Kubernetes-based platform services and infrastructure for model inference and sandboxing.
You will build agentic workflows, document processing pipelines, and retrieval systems to solve complex client problems using AI. You will drive projects end-to-end, from discovery through deployment, while collaborating closely with both internal and client teams.
