Senior Backend AI Systems Engineer

We are partnering with an enterprise client to architect high-throughput backend infrastructure, asynchronous pipelines, and low-latency API gateways powering enterprise Generative AI applications. We are seeking an experienced Backend AI Systems Engineer to represent our organisation and take technical ownership of backend service design, vector retrieval engines, and distributed model serving infrastructure.

In this role, you will bridge systems-level backend engineering and foundation model orchestration within our client’s ecosystem. You will be instrumental in building high-concurrency microservices, managing semantic search indexes, optimising API streaming performance (SSE/WebSocket), and integrating enterprise AI workflows into secure cloud backends.

As the employer of record, we provide full visa and migration support for qualified engineering talent deployed to our clients:

• 482 On-Hire Sponsorship Transfers: Fully supported for qualified candidates currently in Australia on an existing 482 visa looking to transfer sponsorship to work with our clients.
• New 482 Visa Sponsorship: Available for qualified candidates meeting commercial experience and technical requirements.

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• Temporary & Working Visa Holders: Open to all working visa holders seeking a direct pathway to employer sponsorship.

• Backend Microservices & APIs: Design, build, and maintain high-concurrency, asynchronous backend APIs using Python (FastAPI / AsyncIO) or Go, handling complex data validation and event streaming.
• LLM Orchestration & Context Engines: Architect resilient backend orchestration layers, structured tool-calling systems, and Retrieval-Augmented Generation (RAG) pipelines using LangChain, LlamaIndex, or native SDKs.
• Vector Search & Embedding Systems: Configure, tune, and scale production vector databases and hybrid search architectures (pgvector, Pinecone, Qdrant, or Weaviate).
• Distributed Task & Message Queues: Manage asynchronous background jobs, caching, and rate limiting using Redis, Celery, RabbitMQ, or Apache Kafka.

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• Cloud & Container Deployment: Containerise backend workloads via Docker and deploy horizontally scalable services on Kubernetes (EKS/AKS) or cloud container platforms (AWS ECS/Azure Container Apps).
• Observability & Guardrails: Implement backend tracing, token-cost metering, latency tracking, and evaluation logging across AI API calls (OpenTelemetry, Langfuse).

• Backend Engineering Mastery: Proven commercial track record building scalable, asynchronous backend systems and high-traffic APIs (preferably Python / FastAPI).
• AI & LLM Integration: Hands-on experience integrating frontier LLMs (OpenAI, Anthropic Claude) into production backends, including token streaming and JSON schema outputs.
• Vector & Data Stores: Deep practical experience with SQL databases (PostgreSQL), Redis caching, and vector indexing (pgvector, Pinecone, or Qdrant).

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• Concurrency & Systems Design: Strong understanding of event loops, multi-threading/async patterns, connection pooling, and distributed rate limiting.
• Location Requirements: Currently residing in Australia with valid work rights or eligibility for 482 visa sponsorship/transfer.

• Hands-on experience deploying open-source models using vLLM, Ollama, or Triton Inference Server.
• Familiarity with agentic loop design patterns (LangGraph, AutoGen).
• Commercial experience with Go or Rust for high-performance systems programming.