Principal AI .NET Engineer – Architecting AI-Driven Backend

Software@Scale; Sydney; On-site; AUD 180,000 - 260,000; Full time

Benefits offered by this job

Access to AI tooling

Growth pathways

Low ceremony environment

Competitive remuneration

Job summary

Software at Scale seeks an exceptional Principal AI .NET Engineer to lead architectural directions for AI-enabled backend platforms. You will own production-grade AI agentic workflows, seamlessly integrating Claude-like models into robust .NET services and ensuring secure, scalable operations inside AWS.

You will guide engineering teams, champion best practices, and drive performance optimisations across high‑throughput APIs and distributed workflows, while leveraging enterprise AI tooling to

Qualifications

• Proven commercial experience at principal or senior staff level.
• Hands-on experience building production-grade AI agents and orchestration systems.
• Deep experience integrating non-deterministic AI models into enterprise .NET services.

Responsibilities

• Provide architectural leadership and uplift engineering squads.
• Design and build autonomous AI agentic workflows and production AI platforms.
• Integrate AI models (Claude or similar) into core .NET services with robust JSON handling.
• Evaluate AI performance, latency, token efficiency, and drift; tune for reliability.
• Architect high-throughput RESTful APIs and distributed workflows in AWS.
• Deploy and operate .NET workloads on AWS (ECS, Secrets Manager) with solid DevOps practices.

Skills

Principal Leadership

AI Agentic Eng

LLM Integration

AI Evaluation

NET Mastery

DevOps Mastery

Tools

AWS ECS

SQS/SNS

Secrets Manager

Job description

Software at Scale seeks an exceptional Principal AI .NET Engineer to lead architectural directions for AI-enabled backend platforms. You will own production-grade AI agentic workflows, seamlessly integrating Claude-like models into robust .NET services and ensuring secure, scalable operations inside AWS.

You will guide engineering teams, champion best practices, and drive performance optimisations across high‑throughput APIs and distributed workflows, while leveraging enterprise AI tooling to