Senior Software Engineer

Senior Software Engineer AI Development

Location: Australia (remote-first AU-based)

Employment type: Full-time

Salary: Competitive senior market rate

The problem space

Refactor are partnering with an Australian AI startup with a production AI platform enterprise customers and a growing SaaS offering.

Were now extending the platform to support an LLM-agnostic agentic AI assistant that integrates across enterprise systems and helps everyday business users plan work prioritise tasks and structure their work experience.

This role exists because the system is no longer experimental. Retrieval quality orchestration latency security and UX are all priorities in our ongoing product development.

What were building

The platform is built around production-grade RAG and agentic workflows.

Backend:

A Java-based API platform that:

• Orchestrates LLM calls
• Executes multi-step agent workflows
• Integrates with enterprise systems
• Enforces tenancy security and access controls

Retrieval layer

• Weaviate as the primary vector database
• Hybrid search (vector keyword)
• Tuned chunking embeddings and reranking

Frontend:

• React application used daily by non-technical business users
• Surfaces agent outputs plans tasks and recommendations

AI layer

• LLM-agnostic (supporting models from Anthropic Mistral & Open AI)
• Support for cloud private and sovereign deployments
• Explicit cost latency and reliability controls

The assistant is agentic by design. The planning sequencing actions querying multiple systems and producing structured outputs users can act on.

What youll be responsible for:

Youll be a hands-on senior engineer contributing across backend and frontend with a primary focus on core product development.

Backend & product engineering:

• Strong T-shaped engineering skills with deep expertise in backend Java development and practical knowledge across related technologies required to deliver production-ready systems
• Experience with AWS and cloud technologies CI/CD pipelines system configuration unit and manual testing and integrations with external systems such as web crawlers and enterprise API services
• Developing LLM orchestration and agent execution layers
• Implementing and maintaining enterprise integrations (documents tasks calendars knowledge systems)
• Ensuring APIs are versioned stable and suitable for enterprise time horizons

RAG & retrieval systems:

• Designing and evolving production RAG pipelines

Owning:

• Document ingestion and preprocessing
• Chunking strategies and embedding lifecycle management
• Retrieval tuning hybrid search and reranking
• Experience in database-level optimisations query and retrieval performance tuning and embedding lifecycle management to improve accuracy consistency and efficiency in production datasets

• Improving grounding relevance and consistency across real customer datasets
• Handling retrieval failure modes and partial or stale data scenarios

System quality scale & reliability

• Optimising latency throughput and cost across retrieval and model calls
• Implementing observability (structured logging metrics tracing)
• Building guardrails timeouts and fallback behaviour for agent workflows
• Expertise in observability structured logging metrics tracing and automated testing strategies to ensure robust scalable and reliable production systems
• Contributing to tenant isolation data boundaries and security controls

Frontend contribution (important but secondary):

• Practical understanding of frontend requirements enabling close collaboration with UI teams to align API design and deliver fully integrated production-ready features
• Translating backend capabilities into clear usable interfaces for non-technical users
• Contributing directly to the React codebase where appropriate
• Ensuring tight alignment between API design and frontend needs

React experience is not required but practical exposure is a strong advantage.

Product-driven engineering

• Translating real user workflows into concrete product features
• Making pragmatic trade-offs between accuracy explainability performance and UX
• Iterating based on production feedback not assumptions

What were looking for

Were looking for engineers who have already built and managed sovereign AI deployments in production environments.

Required:

• 5 years professional software engineering experience
• 2 years building RAG-based systems used in production
• Strong backend experience

• Hands-on experience with:
• Vector databases
• Embeddings chunking and retrieval strategies
• LLM orchestration (framework-based or custom)
• Experience supporting production customer-facing systems

Valued:

• Experience with retrieval quality and failure modes
• Experience with agentic workflows beyond simple chains
• SaaS or multi-tenant architectures
• React experience or strong API-first frontend collaboration
• Enterprise or regulated environments
• AWS private cloud or hybrid deployments

Why this role might be interesting

• Youll work on real RAG and agentic systems designed for scale. Youll be working at the cutting edge as open source models mature helping evaluate and deploy the best-fit models responsibly in private enterprise environments
• Youll be involved with evolving the backend architecture and user-facing product
• Customers are already live so your work has immediate impact
• Small senior geo-distributed team with high autonomy and professionalism
• Australian company building globally relevant enterprise AI

When you apply were interested in:

• What RAG systems youve built or operated
• Where they struggled or failed
• How you approached trade-offs in retrieval latency and UX
• What kind of AI product you want to help build next

This role is a fantastic opportunity to join a growing start up who have already landed some major accounts and now ready to expand. Youll join an exisiting team who is increibly talented.

What are you waiting for - get applying now!

James Farrey

Founder & Director

#SCR-james-farrey