Lead AI Engineer – AI Systems, Automation

BJAK

Lead AI Engineer designing and developing production AI systems for insurance workflows in a remote environment. Collaborate with global teams to build reliable and scalable AI capabilities.

Posted 6/28/2026full-timeRemote • 🇰🇷 South KoreaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and developing AI systems for workflow automation, with a strong focus on system architecture, debugging, and observability. Proven ability to lead cross-functional teams and drive technical execution in high-scale environments.

Highest-signal resume keywords

AI System DesignDistributed SystemsInference PipelinesObservability SystemsTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI EngineeringBackend DevelopmentSystem DesignDebuggingAI OrchestrationProduction EnvironmentsLatency OptimizationScalabilityIncident ResponseAutomation Workflows

Soft Skills

Ownership MindsetCollaborationMentoringProblem-SolvingCommunication

Tools & Technologies

LoggingMonitoringTracingAlerting

Industry Keywords

AI SystemsLLMsEmbeddingsRecommendation SystemsWorkflow Automation

Tech Stack

Tools & technologies

Distributed Systems

About the role

Key responsibilities & impact

Lead the design and development of production AI systems powering insurance workflow automation.
Architect AI orchestration layers connecting LLMs, backend services, and business workflows.
Own end-to-end AI system design, including inference pipelines, routing, caching, and fallback strategies.
Drive engineering decisions around latency, reliability, cost, and scalability of AI services.
Lead implementation of observability systems (logging, monitoring, tracing, alerting).
Review and guide backend AI implementation across engineering teams.
Collaborate with product, backend, DevOps, and operations teams to ship end-to-end AI features.
Debug complex production issues across distributed AI systems and lead root-cause analysis.
Define engineering standards and best practices for AI system development.
Mentor engineers and elevate technical execution quality across teams.

Requirements

What you’ll need

Strong backend or AI engineering experience in production environments.
Experience building or scaling AI systems (LLMs, embeddings, recommendation systems, or automation workflows).
Strong system design skills with experience in distributed systems.
Experience with production inference pipelines and AI orchestration.
Strong debugging ability in high-scale, latency-sensitive environments.
Experience with observability, monitoring, and incident response.
Proven ability to lead technical execution in cross-functional teams.
Strong ownership mindset and ability to drive projects end-to-end.
Comfortable working in fast-paced, globally distributed teams.

Benefits

Comp & perks

Build AI-Powered Products - Work on intelligent insurance automation systems.
Global Engineering Organization - Collaborate across multiple countries.
International Impact - Products used by millions across Southeast Asia and beyond.
Learning & Development Budget - Support continuous technical growth.
High Ownership Culture - Lead critical AI systems end-to-end.
Modern Engineering Practices - Strong focus on scalability and reliability.
Leadership Impact - Shape AI architecture and engineering standards.
Competitive Compensation - Attractive salary package.
Fully Remote - Work remotely with globally distributed teams.