Forward Deployed Engineer

Zendesk; City of Melbourne; On-site; AUD 120,000 - 180,000; Full time

Job summary

Zendesk is hiring a Forward Deployed Engineer (FDE) to join our Technical Presales team in Melbourne, Australia. You will embed with strategic customers to design, build, and deliver production-grade AI solutions that unlock measurable ROI and improve real-world workflows.

You'll lead end-to-end technical delivery of AI agent and LLM-powered solutions, build robust integrations, troubleshoot blockers, and collaborate with Sales engineering and Product teams to map systems and drive adoption

Qualifications

• 5+ years of software engineering delivering production systems.
• Proficient in Python and JavaScript/TypeScript; experience with at least one backend language (Java, Ruby, or Go) preferred.
• Hands-on experience deploying or integrating LLM-based solutions in production (RAG, vector DBs, model evaluation).
• Practical experience with cloud platforms (AWS/GCP/Azure) and containerized deployments (Docker, Kubernetes).
• Willing to travel up to 50% and onsite with customers.

Responsibilities

• Lead end-to-end technical delivery of AI agent and LLM-powered solutions: discovery, scoping, system design, PoC, production rollout, and operational handoff.
• Build production-grade integrations and custom connectors (APIs, data pipelines, RAG systems) to surface high-quality signals into Zendesk AI workflows.
• Troubleshoot and resolve escalated technical blockers across data, infra, and model behavior; act as the field’s final line of defense for complex issues.
• Collaborate with Sales engineering and product teams to map undocumented systems, define integration patterns, and implement solutions.
• Translate customer learnings into product feedback and prioritized roadmap items; partner with Zendesk Product and Engineering to reduce future deployment friction.

Skills

Software engineering

Python

TypeScript

React

LLM expertise

Data pipelines

Kubernetes

AWS/GCP/Azure

REST/GraphQL

Communication

Tools

LangChain

LlamaIndex

Haystack

Docker

Kubernetes

Job description

**Forward Deployed Engineer (FDE) — AI & Solutions**Join us at Zendesk, where we're on a mission to power exceptional service for every person on the planet. We're accelerating that ambition by building products rooted in AI, automation, and intelligent customer experiences, because behind every interaction lies an opportunity to make a human connection.We’re hiring a Forward Deployed Engineer (FDE) to join our Technical Presales team. Where you’ll be an elite hybrid: part software engineer, part strategic consultant. You’ll do more than just sell the dream, you’ll execute what’s possible. In this role you’ll be embedded with strategic customers to design, build, and deliver production-grade AI solutions that unlock measurable ROI. You’ll translate customer needs into robust integrations, architect LLM-powered workflows, and collaborate cross-functionally to turn real-world friction into product improvements.You will play a vital role in growing Zendesk’s enterprise footprint by delivering high-impact, scalable AI deployments. Building trusted technical partnerships, delivering crisp, production-ready solutions, and amplifying Zendesk’s AI capabilities within complex, high-value accounts.**What you’ll be doing*** Lead end-to-end technical delivery of AI agent and LLM-powered solutions: discovery, scoping, system design, PoC, production rollout, and operational handoff.* Build production-grade integrations and custom connectors (APIs, data pipelines, RAG systems) to surface high-quality signals into Zendesk AI workflows.* Troubleshoot and resolve escalated technical blockers across data, infra, and model behavior. Act as the field’s final line of defense for complex issues.* Collaborate closely with Sales engineering and product teams to map undocumented systems, define integration patterns, and implement solutions.* Translate customer learnings into product feedback and prioritized roadmap items; partner with Zendesk Product and Engineering to reduce future deployment friction. **What you bring to the role*** **Founder mindset:** comfortable navigating ambiguity, mapping unknown systems, and delivering results without a predefined playbook.* **Engineering rigor:** 5+ years building production systems; strong software engineering fundamentals and attention to quality.* **Full-stack fluency:** experience across frontend (React/TypeScript), backend (Python / Node / Java / Ruby), REST/GraphQL APIs, and cloud infrastructure (AWS, GCP, or Azure).* **AI/LLM expertise:** practical experience with LLMs, prompt engineering, retrieval-augmented generation (RAG), vector stores, and evaluation/guardrails for model safety and reliability.* **Systems integrator:** proven ability to design secure data ingestion, ETL/data pipelines, and work with diverse data stores and message systems.* **Excellent communicator:** able to present trade-offs to executives and pair-program with customer engineers; strong written runbooks and documentation skills.* **Travel readiness:** willing to travel as needed to customer sites (typical range 25–50%).**Basic qualifications** * 5+ years software engineering experience delivering production systems.* Proficient in Python and JavaScript/TypeScript; experience with at least one backend language (Java, Ruby, or Go) preferred.* Hands-on experience deploying or integrating LLM-based solutions in production (RAG, vector DBs, model evaluation).* Practical experience with cloud platforms (AWS/GCP/Azure) and containerized deployments (Docker, Kubernetes).* Comfortable working onsite with customers and traveling up to 50% as required.**Preferred qualifications*** Prior FDE, Professional Services, Solutions Engineering, or Technical Account Management experience delivering enterprise AI or SaaS deployments.* Experience with specific LLM tooling and frameworks (LangChain, LlamaIndex, Haystack, or similar).* Familiarity with Zendesk products and APIs, or experience integrating customer support/contact-center platforms.* Background in observability and operationalization of ML/AI (monitoring, SLOs, cost controls, retraining pipelines).* Experience with data governance, PII handling, and enterprise security/compliance constraints.