Senior Backend AI Engineer

AutoGrab is on a mission to reshape the automotive industry with data-driven, intelligent technology. Backed by leading investors and trusted by major OEMs, marketplaces and dealer groups, we're building the infrastructure powering the future of vehicle trading.

We have been recognised in the AFR Fast Starters, LinkedIn's top 25 Startups and Deloitte Tech Fast 50 lists, reflecting our rapid growth, strong market demand and relentless pursuit of innovation.

About the Role

This is a senior engineering role at the intersection of backend engineering and agentic AI. You will work within AutoGrab's engineering team, reporting to the Tech Lead - Backend Engineering, to build and operate LLM-driven backend applications and agentic workflows that sit at the core of our product intelligence.

We are looking for someone who is genuinely cross-disciplinary, equally at home in the backend service context as in the agentic LLM space. You will bring strong applied experience in building production agentic systems, combined with deep backend engineering fundamentals across deployed services and data-intensive applications.

This is a hands-on engineering role. You will write production code, shape technical decisions and contribute meaningfully to a small, high output team that moves quickly and values quality. While you will also use agentic tooling to write code, you'll need to be able to understand and write code by hand, as well as craft the details of an architecture and technical solution design.

Key Responsibilities

Backend Engineering

• Write, review and understand well-structured, tested, production-quality code, stepping in with your own judgement where agentic coding tools fall short

• Build and maintain production-grade backend services that serve and process AutoGrab's live data

• Work across a variety of databases, SQL and data-intensive applications, both stream and batch oriented

Agentic AI Engineering

• Design, build and operate agentic LLM workflows and data applications in production

• Apply prompt engineering, RAG and vector search, and tool/function calling in a data pipeline or data analysis context

• Own LLM evaluation and observability, and manage inference scalably, reliably and cost-effectively

• Contribute to guardrail design and compliance-sensitive flow handling within AutoGrab's AI systems

Infrastructure & Operations

• Deploy and operate LLM-related services on GCP, including Vertex AI, Cloud SQL and Cloud Run/GKE

• Work with infrastructure-as-code (Terraform) and CI/CD pipelines to maintain operational discipline

• Contribute to system reliability, cost management and observability across production services

Collaboration & Communication

• Work through ambiguity with the team and technical leadership, making trade-offs clearly and setting expectations honestly

• Collaborate closely with engineers across backend, data science and product disciplines

• Contribute to engineering discipline: code review, testing standards and technical documentation

About You

Required

• Hands-on programming and solution design experience, and genuine care for the engineering craft

• Experience building and deploying production-quality backend services

• Strong experience with a variety of databases, SQL and data-intensive applications, both stream and batch oriented

• Genuinely cross-disciplinary across agentic LLM-driven and backend applications, this is the core of the role

• Strong applied experience building agentic LLM workflow systems in production: prompt engineering, RAG, vector search and tool/function calling, ideally in a data pipeline or data analysis context

• Solid understanding of LLM evaluation, observability and how to manage inference scalably, reliably and cost-effectively

• Experience deploying and operating LLM-related services on GCP (Vertex AI, Cloud SQL, Cloud Run/GKE), with infrastructure-as-code (Terraform) and CI/CD

• Excellent communication and collaboration skills, comfortable working through ambiguity, making technical trade-offs and setting expectations honestly with the team and leadership

Preferred

• Experience with LLM fine-tuning or training embedding models

• Experience using LLMs for search, and evaluating search systems

• Familiarity with evaluation-driven development for LLM products and guardrail/safety patterns for compliance-sensitive use cases

• Familiarity with Helm, ArgoCD or Kubernetes

• Experience with Python data science and data analysis tools

Why AutoGrab

• Join a high-growth technology company building something genuinely novel at the intersection of automotive data and AI

• Work in a small, high-output engineering team where your contribution has direct product impact

• Hands-on, collaborative culture with real exposure to agentic AI systems in production

• Report directly to the Tech Lead -Backend Engineering, with clear lines to senior technical leadership

• Offices in Australia, the UK and Asia with global expansion under way

• Competitive remuneration and the opportunity to grow with the business