Real-Time Distributed Backend Engineer for Drones

Intelix.AI; Washington (District of Columbia); Hybrid; USD 145,000 - 275,000; Full time

Job summary

Intelix.AI is hiring for a backend/service-layer engineer to design cloud-and-edge middleware that links onboard drone software, a 3D operator UI, a physics/simulation engine, and an LLM/agent layer. You will build real-time data pipelines using Kafka, NATS, gRPC, and pub/sub, and ship scalable microservices with robust fault tolerance.

The role requires hands-on work across cloud and edge environments, including Docker and Kubernetes deployments, with potential collaboration on autonomous

Qualifications

• 4–10 years backend / service-layer engineering with hands-on experience.
• Built and owned middleware, APIs or microservices integrating multiple systems.
• Experience with real-time data and messaging systems in production (Kafka, NATS, gRPC, pub/sub).
• Proficient in modern backend languages; Rust preferred but not required.

Responsibilities

• Design and ship cloud-and-edge middleware connecting onboard drone software, the 3D operator UI, the physics/simulation engine, and the LLM/agent layer.
• Manage real-time data and messaging, streaming and pub/sub relays across external services.
• Develop scalable, reliable microservices with fail-safe behavior.
• Handle service registration and discovery across distributed deployments and deployments across cloud/edge.
• Ship to real field hardware as well as cloud (Docker, Kubernetes, edge).
• Iterate from prototype to field with customers, handing off to core teams as foundations mature.

Tools

Kafka

NATS

gRPC

pub/sub

Docker

Kubernetes

Rust

Python

C++

Java

Job description

Intelix.AI is hiring for a backend/service-layer engineer to design cloud-and-edge middleware that links onboard drone software, a 3D operator UI, a physics/simulation engine, and an LLM/agent layer. You will build real-time data pipelines using Kafka, NATS, gRPC, and pub/sub, and ship scalable microservices with robust fault tolerance.

The role requires hands-on work across cloud and edge environments, including Docker and Kubernetes deployments, with potential collaboration on autonomous