Principal Software Engineer | AI at Simple Machines

Sydney, New South Wales, Australia -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Software Engineering, Backend Development, Full-Stack Development, Agentic Systems, LLM Workflows, LangGraph, LangChain, API Integration, Data Services Integration, System Design, Performance Tuning, Testing, Deployment, Application Logic, Orchestration Layers, Production Engineering

Industry

Information Technology & Services

Description

Simple Machines is a leading independent boutique technology and consulting firm with a global presence, including teams in Sydney, London, Poland and New Zealand. We specialise in architecting and engineering scalable data platforms and software systems designed for an AI-driven world. By combining strong engineering discipline with applied AI, we transform raw data into practical, high-value solutions. We Engineer Data to Life. Our mission is to help enterprises, technology companies, and governments better connect with and understand their organisations, their people, their customers, and citizens. We are a senior only consultancy of engineers, architects, and advisors who care deeply about clarity, craft, and outcomes. The Opportunity This is an opportunity to work at the leading edge of applied AI engineering, building real-world systems that move beyond prototypes into production. You’ll help shape how modern organisations operationalise LLM driven capabilities designing reliable, scalable software that integrates intelligent workflows into core products and platforms. Rather than focusing on model research, the emphasis is on solving complex engineering problems, productionising emerging frameworks, and turning fast-evolving AI concepts into durable, maintainable solutions. The Role As a Principal Software Engineer, you will design and build backend or full-stack agentic systems that orchestrate LLM-powered workflows using frameworks such as LangGraph or LangChain. You’ll develop agentic pipelines, integrate APIs and data services, and implement robust application logic that enables reasoning-driven automation at scale. The role requires strong engineering discipline system design, performance, testing, and deployment more aligned to a data or platform engineer mindset than a data scientist. You will work hands-on across architecture and implementation to deliver production-grade intelligent solutions. Key Requirements Strong Software Engineering Background We are looking for experienced engineers with hands-on development expertise across full-stack or server-side environments. LLM Application Framework Experience Demonstrated experience with at least one of the following is required: LangGraph or LangChain, with a focus on building production-grade applications rather than experimentation. Agentic / Workflow-Oriented Architectures Experience designing or implementing agentic workflows, orchestration layers, or multi-step reasoning pipelines is highly valued. Model Context Protocol (MCP) Familiarity (Desirable) Exposure to MCP or similar patterns is a strong indicator of alignment with the type of systems we build. Engineering-Focused (Not Data Science) This role is centred on application engineering and system integration, not model research or training. Why Join Simple Machines? You’ll work on interesting, high-impact problems You’ll build modern platforms, not maintain legacy mess You’ll be surrounded by senior engineers who actually know their craft You’ll have autonomy, influence, and room to grow If you’re a senior software engineer who wants to build properly, think clearly, and deliver real outcomes - we should talk.

Responsibilities

The Principal Software Engineer will design and build backend or full-stack agentic systems that orchestrate LLM-powered workflows using frameworks like LangGraph or LangChain. This involves developing agentic pipelines, integrating necessary APIs and data services, and implementing robust application logic for reasoning-driven automation at scale.