Tech Lead - AI Platform

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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).