This is a remote position.

Position Overview:

We are seeking an elite Lead Full Stack AI Engineer to design and build the operator control plane for our enterprise Agentic Ops platform. In this role, you will connect real-time web interfaces with asynchronous multi-agent backends—turning complex agent reasoning, dynamic network topologies, and automated remediation runs into intuitive, transparent interfaces for IT and SOC operators. You will bridge frontend performance with up-to-date AI frameworks, NVIDIA/Cisco integrations, and open-source tooling.

Core Responsibilities:

- Operator Cockpit & HITL Control: Build the primary Agentic Ops console with real-time telemetry streaming, interactive multi-agent reasoning traces, execution dry-runs, and human-in-the-loop (HITL) approval/override controls.

- Dynamic Topology & DAG Visualization: Develop high-performance WebGL/canvas-based visual interfaces to render large-scale enterprise network maps, asset dependencies, and live multi-agent execution graphs without UI lag.

- Low-Latency Streaming Pipelines: Engineer bidirectional streaming protocols (Web Sockets, SSE, gRPC-Web) to handle real-time agent thought streams, tool invocation payloads, and live terminal outputs.

- BFF & Edge Middleware: Build performant backend-for-frontend (BFF) layers in Go, Python (FastAPI), or Type Script, implementing session state persistence, token caching, SAML/OIDC authentication, and fine-grained RBAC.

- AI Tooling & Ecosystem Integration: Connect the UI layer to agent runtimes, open-source automation tools (Open Claw, Browser-Use), and model endpoints powered by NVIDIA NIMs, vLLM, and local SLMs.

- AI-Augmented SDLC:



Utilize AI-assisted development workflows, automated component scaffolding, synthetic UI testing, and continuous eval pipelines to accelerate delivery.

Required Qualifications:

- Experience & Track Record: 8+ years building enterprise-grade full-stack web applications, with 3+ years focused on AI/LLM-native interfaces, observability platforms (APM/SIEM), or network operations tooling.

- Frontend Architecture: Deep mastery of Type Script, React / Next.js, Tailwind CSS, state management (Zustand, Tan Stack Query), and rendering optimization for real-time streaming data.

- Graph & Canvas Visualization: Production expertise with canvas/SVG/WebGL visualization libraries (React Flow, Cytoscape.js, PixiJS, D3.js) for large-scale hierarchical graphs and multi-step agent reasoning trees.

- AI & Agentic Ecosystem: Hands-on experience with modern agent frameworks (Lang Chain, Lang Graph, Vercel AI SDK), open-source agentic tools (Open Claw, Browser-Use), and consuming endpoints from NVIDIA NIMs, vLLM, or Ollama.

- Streaming & Real-Time Protocols: Production fluency with SSE, Web Sockets, and chunked streaming, managing partial token rendering and optimistic UI states under network latency.

- UX for Autonomous Systems: Strong design intuition for non-deterministic software:



progressive disclosure of reasoning, confidence/risk scoring, before-and-after diff previews, and safety-critical confirmation workflows.

Mandatory:

Experience

- 8+ years building enterprise-grade full-stack web applications

- 3+ years on AI/LLM-native interfaces, observability platforms (APM/SIEM), or network operations tooling

Frontend

- Type Script, React / Next.js, Tailwind CSS

- State management: Zustand, Tan Stack Query

- Rendering optimization for real-time streaming data

Graph & Visualization

- Production experience with at least one of React Flow, Cytoscape.js, PixiJS or D3.js (canvas/SVG/WebGL)

- Rendering large hierarchical graphs and multi-step agent reasoning trees

Real-Time Streaming

- SSE, Web Sockets and chunked streaming

- Partial token rendering and optimistic UI under network latency

Backend / BFF

- Backend-for-frontend layers in Go, Python (FastAPI) or Type Script

- SAML/OIDC authentication, RBAC, session state and token caching

AI & Agentic Ecosystem

- Agent frameworks: Lang Chain, Lang Graph or Vercel AI SDK

- Consuming model endpoints from NVIDIA NIMs, vLLM or Ollama

UX for Autonomous Systems

- Human-in-the-loop (HITL) approval/override workflows

- Progressive disclosure of reasoning, confidence/risk scoring, diff previews and safety-critical confirmations

Good to Have (Optional)

- gRPC-Web

- Open Claw, Browser-Use

- Local SLM deployment

- NVIDIA/Cisco platform integrations

- AI-assisted SDLC: component scaffolding, synthetic UI testing, continuous eval pipelines

📌 Lead Fullstack AI Engineer (India)
🏢 netweb software
📍 India