Forward Deployed Engineer (AI)

Location: Australia or Singapore

Company

hirify.global builds AI products and applications, including coding and agent-powered workflows.

What you will do

• Lead customer discovery, understand codebases and workflows, identify valuable use cases, and define success metrics.
• Design and ship production AI agents, applications, and coding-agent workflows using Grok and related models.
• Integrate tools, MCP servers, retrieval, rules, skills, CI gates, and evaluations with customer authentication, data, workflows, and delivery systems.
• Launch initial versions quickly and improve them through rollout, monitoring, evaluation, debugging, and iteration.
• Own production quality, including tracing, reliability, incident response, latency, and cost tradeoffs.
• Measure business outcomes, enable customers to operate delivered systems, and turn field learnings into reusable product improvements.

Requirements

• 5+ years of experience in software engineering, machine learning engineering, or data science.
• Production coding and code review experience with Python and JavaScript/TypeScript.
• Experience building and owning AI-native workflows or agents in production and debugging model, tool, data, pipeline, or delivery failures.
• Experience with production reliability, including metrics, alerts, safe rollouts, and incident response.
• Ability to build end to end across frontend, backend, infrastructure, and prompt iteration.
• Ability to work through ambiguity and communicate effectively with engineers and VP-level stakeholders.

Nice to have

• 2+ years in a customer-facing role with responsibility for external stakeholder outcomes.
• Hands-on experience with agents and tool-calling, evaluation harnesses, retrieval and context systems, CI/CD or developer platforms, and AI-native coding workflows.
• Experience as a forward-deployed, solutions, or platform engineer embedded with customers, or as a founding engineer owning an AI product end to end.

Culture & Benefits

• End-to-end ownership from discovery through launch, iteration, and post-launch support.
• Direct collaboration with customer engineering, platform, domain, and leadership teams.
• Work focused on measurable outcomes such as revenue, cost, hours saved, error rate, cycle time, and escaped defects.