Tech Lead - AI Platform

The Tech Lead — AI Platform is the senior technical leader for the platform runtime, AI engine, and agent-orchestration tier of an AI-native retail decisioning platform. The role is accountable for architectural integrity, build vs buy decisions, integration with upstream data and knowledge-graph services, and the agent runtime's safety and scale properties — and leads a team of senior software, AI, and ML engineers. 

Remote candidates outside of Thailand are welcome to apply.

Key Responsibilities:

• Own the reference architecture for the platform's runtime, decisioning, and agent-execution tiers; co-chair the Architecture Review Board; author Architecture Decision Records. 
• Design, build, and operate the agent runtime (LangGraph, CrewAI, or chosen framework) — deployment, scale, observability, cost per invocation. 
• Design and ship an agent autonomy framework with progressive trust levels (shadow / recommender / executor patterns) and measurable gate criteria; operationalise human-in-the-loop patterns for every agent. 
• Own the agent registry — catalogued, versioned, owned, gated, monitored agents. 
• Define and operationalise the consumption contract with upstream knowledge-graph, semantic-layer, data-product, and event-stream services from the platform team. 
• Lead the AI-side decisioning components — orchestrator, trust gate service, agent-side helpers — and coordinate consumption of LLM Gateway and Vector Search services. 
• Lead a team of senior software, AI, and ML engineers; mentor on agent-engineering discipline; partner with peer Tech Leads on handoffs into application and experience layers. 
• Own runtime SLOs — invocation P95, success rate, HITL response time — and per-agent cost meter; lead incident response for runtime degradation. 

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline. 
• 8+ years software engineering with 3+ years in a Tech Lead / Staff role owning platform standards. 
• Production agentic systems experience — multi-agent orchestration, HITL gates, eval-driven CI; not just RAG demos. 
• Strong distributed-systems fundamentals — concurrency, message queues, observability, performance. 
• LLM platform depth — at least one major provider (Azure OpenAI, Anthropic, Bedrock, Vertex) in production with cost / latency optimisation. 
• API-first design discipline — service contracts, SLOs, versioning, deprecation policies. 
• Cloud platform experience (Azure preferred; AWS / GCP transferable). 
• Architectural authorship — has written ADRs, chaired ARB, made build-vs-buy calls with executive sponsors. 

Preferred Qualifications:

• Built or led a production multi-agent platform serving multiple business consumers. 

• Open-source agent framework contributions (LangChain / LangGraph / AutoGen / DSPy). 

• Retail / commerce / fintech domain experience; knowledge-graph production experience (Neo4j, Neptune, TigerGraph). 

• Causal inference exposure (DoWhy / EconML).