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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.
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.
Qualifications
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.
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).