AI Platform Engineer

Amunra

Mumbai, IND

Full-time

Entry

Onsite

Discovered 1 weeks ago

AI platform engineeringKnowledge systemsHybrid knowledge architectureKnowledge graphsRetrieval systemsAgent memory

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AI platform engineeringKnowledge systemsHybrid knowledge architecture

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Role Overview

Amunra is seeking a Senior AI Platform and Knowledge Systems Engineer.

Build the software layer that makes complexity-science and dynamic-systems models persistent, queryable, and usable by AI agents and institutional clients.

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

Key Responsibilities

Build a hybrid knowledge architecture combining graphs, structured data, and retrieval to represent system states, dependencies, feedback loops, transitions, simulations, and historical outcomes.
Develop working, episodic, and semantic memory for agents to recall prior system states, simulations, and decisions across sessions.
Expose mathematical and simulation models as reliable, typed tools through APIs, gRPC, Protocol Buffers, and protocols such as MCP.
Build tool registries, context assembly, workflow and state management, and permission-aware agent interaction with complexity-science models.
Design secure private deployment patterns using AWS PrivateLink, VPC peering, mTLS, private APIs, and client-managed environments.
Ensure strict segregation of client data, agent context, simulations, and memory across tenants.
Build end-to-end tracing of retrieved context, model calls, simulation outputs, and agent actions.
Develop Python and TypeScript interfaces for institutional and machine-to-machine access.

Technical Scope

Knowledge architecture combines graphs, structured data, and retrieval.
Agent infrastructure includes tool registries, context assembly, workflow and state management, and permission-aware interaction.
Secure delivery includes AWS PrivateLink, VPC peering, mTLS, private APIs, and client-managed environments.
SDK development uses Python and TypeScript.

Role Context

The engineer sits between scientific models, AI systems, and enterprise infrastructure.
The systems support institutional clients and machine-to-machine access.

Requirements

Systems engineering experience spanning scientific models, AI systems, and enterprise infrastructure.
Experience building knowledge, memory, and delivery layers for AI agents and institutional clients.
Ability to design secure private deployment patterns and client-managed environments.
Ability to implement strict multi-tenant segregation of client data, context, simulations, and memory.
Experience developing Python and TypeScript interfaces for institutional and machine-to-machine access.