Forward-Deployed AI Solutions Engineer

Zendesk Singapore PTE (Singapore); Singapore; Hybrid; SGD 120,000 - 160,000; Full time

Job summary

Zendesk Singapore PTE (Singapore) is seeking a Forward Deployed Engineer to lead AI/LLM-powered customer solutions for enterprise deployments. You will design, build, and rollout production-grade integrations and ensure successful operational handoff with customers.

You’ll travel up to 50% to work onsite with clients, collaborate with Sales Engineering and product teams, and translate customer learnings into roadmap improvements while maintaining engineering rigor and quality.

Qualifications

• 5+ years in production software engineering.
• Proficient in Python and TypeScript; backend in Java/Ruby/Go preferred.
• Hands-on LLM deployment experience (RAG, vector stores, eval).
• Cloud platforms (AWS, GCP, Azure) and container deployments like Docker/Kubernetes.
• Willingness to travel up to 50% to work onsite with customers.
• Strong engineering fundamentals and focus on quality.

Responsibilities

• Lead end-to-end technical delivery of AI agent and LLM-powered solutions.
• Build production-grade integrations and connectors for Zendesk AI workflows.
• Resolve escalated blockers across data, infra, and model behavior.
• Collaborate with Sales Engineering and product teams to map systems and patterns.
• Translate customer learnings into product feedback and roadmap items.

Skills

Python

JavaScript/TypeScript

LLM deployment

RAG

Docker

Kubernetes

Cloud computing

Full-stack React/TS

REST/GraphQL

Communication

Tools

LangChain

LlamaIndex

Haystack

Vector stores

Job description

Zendesk Singapore PTE (Singapore) is seeking a Forward Deployed Engineer to lead AI/LLM-powered customer solutions for enterprise deployments. You will design, build, and rollout production-grade integrations and ensure successful operational handoff with customers.

You’ll travel up to 50% to work onsite with clients, collaborate with Sales Engineering and product teams, and translate customer learnings into roadmap improvements while maintaining engineering rigor and quality.