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

We are looking for a Tech Lead to take technical ownership of our agentic AI product and to lead a small, high-leverage squad that spans data science and front-end engineering.

About the Role

AutoMate is our agentic AI product, a conversational assistant that puts AutoGrab's automotive intelligence directly in the hands of our users. As Tech Lead you will take technical ownership of it and lead the small, dedicated squad that builds it.

This squad is deliberately cross-disciplinary, and that is the heart of the role. It spans a data science stream focused on the AI itself and a front-end stream focused on the user experience, and your central challenge is leading across both. You will coach and direct engineers on either side, and to make the point where the two meet well-designed and reliable. That seam is where AutoMate's quality is won or lost, and it is the part of the system that most needs a single technical owner.

This is a technical leadership role. You will stay hands on, but your primary job is to raise the technical bar, make sound architectural calls, and multiply the people around you.

Key Responsibilities

- Own the technical direction and architecture of AutoMate end to end, from the agent backend through to the front-end experience.
- Lead, coach and direct engineers across two disciplines supporting a data science / Python engineer working in prompts, agents and evaluation, and a TypeScript / React engineer working on the widget and chat UX.
- Own the integration seam between the agent backend and the front end:



the streaming/serving contract (SSE), the surfaced-message and rich-response protocols, tool output schemas that drive structured UI, and agent progress / task streaming. Make this boundary explicit, well-designed and reliable.
- Set the standard for agent quality and safety: LLM evaluation (LangSmith and the multi-tier eval framework), guardrails, hallucination detection, compliance-sensitive flows, and observability.
- Make pragmatic architectural decisions across LangGraph / LangChain / Vertex AI, model selection and cost, latency, caching, session persistence and reliability.
- Uphold engineering discipline: code review, testing, CI/CD, infrastructure-as-code and operational ownership of a production AI service.
- Be accountable for delivery: work with Product to turn an ambitious roadmap into shippable, measurable increments and to be honest about what is achievable within the squad's capacity and protect quality under pressure.

Agentic Engineering

We expect our engineers to work fluently with AI and agentic coding tools, and for this role in particular we expect leadership in it. You will set the example for how the squad uses these tools well.

We are looking for someone operating at the higher end of agentic-engineering maturity: able to decompose ambiguous problems, orchestrate AI tooling effectively across both an agent system and its front end, and stay accountable for the technical decisions.

About You

- 3+ years as technical leader (tech lead, staff or principal-level experience) who is still hands-on and works well with engineers.
- Genuinely cross-disciplinary,



or able to lead across disciplines: you do not need to be the deepest expert in both Python and React , but you must be able to engage credibly with, review and coach engineers in both, and design the interface between them.
- Strong applied experience building LLM / agentic systems in production ideally with LangChain / LangGraph or comparable frameworks, prompt engineering, RAG and vector search, and tool/function calling.
- Exposure to LLM evaluation and observability and to running agents reliably and cost-effectively.
- Experience deploying and operating services on GCP (Vertex AI, Cloud SQL, Cloud Run / GKE), with infrastructure-as-code (Terraform) and CI/CD.
- Excellent communication and stakeholder skills, able to work through ambiguity with Product, set expectations honestly, and explain technical trade-offs to non-technical audiences.
- Fluent, discerning use of AI / agentic coding tools, and the judgement to lead a team in using them well.

Preferred Skills

- Experience with streaming LLM UX (server-sent events), structured / rich agent responses, and session persistence.
- Familiarity with evaluation-driven development for LLM products and guardrail / safety patterns for compliance-sensitive use cases.
- Experience with BigQuery, SQL-generating agents, or analytics-heavy data domains.
- Familiarity with Helm / ArgoCD / Kubernetes and OpenTelemetry-based observability.
- Prior experience leading a small, cross-functional squad and growing engineers at different levels.

Why Join AutoGrab?

- Be part of a high-growth, purpose-driven tech company.
- A supportive and experienced leadership team.
- Dynamic, collaborative culture with real opportunities to grow.
- Offices in Australia, UK, and Asia with global expansion on the horizon.

This is an exciting opportunity to shape the growth engine of a rapid-moving company. If you're interested, we'd love to hear from you!.

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📌 AI Tech Lead (Melbourne)
🏢 AutoGrab
📍 Melbourne