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Lead AI Engineer – AI Systems & Automation
BJAKLead AI Engineer managing the design and development of AI systems. Building scalable workflows and collaborating with global teams at BJAK, Southeast Asia's largest digital insurance platform.
Core Competencies
Role fitCore 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 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 SystemsProduction Inference PipelinesObservability and MonitoringTechnical Leadership
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI EngineeringSystem DesignDebuggingAI OrchestrationLatency OptimizationScalabilityCaching StrategiesFallback StrategiesIncident ResponseAutomation Workflows
Soft Skills
Ownership MindsetCollaborationMentoringProblem-SolvingCommunication
Tools & Technologies
Logging SystemsMonitoring ToolsTracing ToolsAlerting SystemsBackend Services
Industry Keywords
AI SystemsLLMsEmbeddingsRecommendation SystemsProduction Environments
Tech Stack
Tools & technologiesDistributed 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- 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.