Role Overview

We are seeking a Senior AI Platform & Knowledge Systems Engineer to build the software layer that makes our complexity-science and agile-systems models persistent, queryable and usable by AI agents and institutional clients.

You will sit between scientific models, AI systems and enterprise infrastructure, building the knowledge, memory and delivery layer through which agents can understand system state, retrieve historical scenarios, call simulation engines and securely consume model outputs.

This is primarily a systems engineering role, not a prompt-engineering or LLM application role.

Key Responsibilities

- Knowledge & State Plane: Build a hybrid knowledge architecture combining graphs, structured data and retrieval to represent system states, dependencies, feedback loops, transitions, simulations and historical outcomes.

- Persistent Agent Memory: Develop working, episodic and semantic memory so agents can recall prior system states, simulations and decisions across sessions.





- Scientific Model Interfaces: Expose mathematical and simulation models as reliable, typed tools through APIs, gRPC/Protobuf and protocols such as MCP.

- Agent Infrastructure: Build tool registries, context assembly, workflow/state management and permission-aware agent interaction with ICS models.

- Enterprise Delivery: Design secure private deployment patterns using AWS PrivateLink, VPC peering, mTLS, private APIs and client-managed environments.

- Multi-Tenant Isolation: Ensure strict segregation of client data, agent context, simulations and memory.

- Auditability & Observability: Build end-to-end tracing of retrieved context, model calls, simulation outputs and agent actions.

- SDKs: Develop Python and TypeScript interfaces for institutional and machine-to-machine access.

📌 AI Platform Engineer (Mumbai)
🏢 Amunra
📍 Mumbai