Role Description The Head of Retail AI Platform is the senior leader accountable for an AI-native retail decisioning platform end-to-end — strategy, roadmap, delivery, team, governance, and commercial outcome. The platform delivers a portfolio of product suites and agents to merchandising, supply chain, finance, supplier collaboration, and executive-insight users, operating a multi-million-baht programme with a senior engineering team. Remote candidates outside of Thailand are welcome to apply. - Own the platform roadmap end-to-end — phased multi-release delivery, suite sequencing, agent prioritisation, budget vs scope; resolve open strategic and architectural decisions on documented timelines. - Run the platform operating model — Steering Committee, Architecture Review Board, AI governance reviews, cost review, data-product contracts review — with executive sponsors and peer technology directors. - Lead phased multi-release delivery across an 18–24-month horizon — foundation launch, supplier-collaboration suite, retail-operations and executive-insights suites, then commerce-intelligence suite — each with measurable commercial outcomes. - Lead recruitment of the engineering leadership team and the broader IC team; set engineering culture (domain-driven design, agent-native, retail-specific, human-in-the-loop by default). - Own the peer-platform interface — data platform / engineering / governance — operationalise the service contracts (LLM gateway, knowledge graph, data products) and resolve scope-overlap escalations. - Chair governance for agent autonomy progression (shadow / recommender / executor patterns) with measurable criteria and stakeholder sign-off. - Own the commercial narrative with executive sponsors — quarterly business review, ROI tracking, budget burn, run-cost trajectory; defend the platform to the CFO office. - Manage risk — capital exposure on build vs buy, vendor lock-in, regulatory exposure, delivery slippage — with a documented register and clear mitigations. Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, Business, or a related discipline. - 12+ years in software / data / AI engineering with 5+ years in a senior leadership role (Director / Head / VP) accountable for product delivery and team P&L. - Shipped AI-native products at scale — agentic systems, ML platforms, decisioning engines, or comparable — with demonstrable commercial outcomes, not just demos. - Strong technical depth in at least one of: agent runtimes (LangGraph, CrewAI, AutoGen), LLM platforms, classical ML deployment at scale, lakehouse architectures, knowledge graphs. - Programme governance experience — chairing steering committees, presenting to executive sponsors, managing capital budgets at the tens-of-millions scale. - Built and retained engineering teams at 20+ FTE scale in Southeast Asia. - Cross-functional partnership — has navigated peer-platform organisations, Legal / DPO, InfoSec, CFO office. - Excellent written and verbal communication; speaks finance, technology, and business with equal credibility. Preferred Qualifications - Retail technology background — POS, replenishment, merchandising, loyalty, supply chain. - Regulated-AI experience (banking, healthcare, public sector); multi-cloud or hybrid production environments. - Open-source platform leadership; public speaking / thought leadership in AI or agentic systems. - Executive education in AI strategy or digital transformation (MIT Sloan, INSEAD, Stanford or comparable).

Role Description The AI Engineer builds production agents end-to-end on an AI-native retail decisioning platform. The role includes: - Prompt design - Tool definitions - Multi-step workflows on the agent runtime (LangGraph, CrewAI, or chosen framework) - Evaluation harnesses (golden sets, regression gates, multi-step replay) - Human-in-the-loop gate integration - Per-agent cost optimisation Remote candidates outside of Thailand are welcome to apply. Qualifications - Bachelor's or Master's degree in Computer Science, AI / ML, or a related discipline - 5+ years software engineering experience - 2+ years shipping LLM-based or agentic systems to production - Production agent or multi-step LLM workflow experience — LangGraph, CrewAI, AutoGen, DSPy, or custom - Strong Python; comfortable with async, observability, testing - Hands-on with at least one major LLM provider (Azure OpenAI, Anthropic, Bedrock, Vertex) - Eval-driven LLM development — golden sets, LLM-as-judge, regression gates, multi-step replay - HITL gate / agent governance — has shipped agents with explicit gates, not autonomous-by-default - Prompt injection / data leakage / PII handling — designs and tests defences Requirements - Build agents on the platform's agent runtime — prompt design, tool definitions, multi-step workflows, error handling - Ship agents with eval harness, human-in-the-loop gate config, observability instrumentation, cost meter, and runbook - Co-design agent specs with Tech Lead Applications and Suite Product Owners - Partner with ML Engineers on classical ML model integration into agents - Author golden sets per agent — domain-specific test cases capturing must-pass behaviours - Build regression gates in CI so no agent ships without eval-pass - Implement multi-step conversation replay for agents with stateful interactions - Use LLM-as-judge patterns where appropriate; instrument human feedback collection - Configure HITL gates per agent and per agent plan - Implement gate-progression evidence collection (Shadow data, accuracy metrics, override frequency) - Own per-agent cost meter — tokens, vector queries, model inference; report monthly - Tune model routing and implement caching strategies where appropriate - Consume the enterprise LLM Gateway via standard SDK - Partner with platform AI engineering on embedding model selection and retrieval relevance tuning - Mentor seed-programme engineers and contribute to the agent-engineering playbook Preferred Qualifications - Open-source contributions to agent frameworks (LangChain / LangGraph / DSPy) - Multi-agent system at scale in production; retail / commerce / fintech agentic workflows (supplier onboarding, contract intelligence, comparable) - Causal inference exposure (DoWhy / EconML) - Thai-language NLP (PyThaiNLP, WangchanBERTa, SEA-LION, Typhoon) - Vendor certifications such as Databricks Generative AI Engineer or Azure AI Engineer Associate

• Help define Talent Engineering in Ukraine: build AI agents, workflows, and analytics that transform how high-bar recruiting gets done • Build the AI systems that make our Talent Partners unstoppable • You'll know this role is working when: Time-to-first-candidate drops from ~2 weeks to 2-3 days • When a search opens, the first qualified candidate hits the client within days, not weeks • Talent Partners run ~10 high-signal interviews per role, up from ~5, because you've taken manual sourcing off their plate • 3-4 sourcing hypotheses run in parallel from week one - no more sequential testing • Team Tailor becomes the single source of truth • You'll prototype, benchmark, and ship weekly, and document what works and what doesn't • Expect to shadow live searches for the first 2-3 weeks before making any changes

• Own and evolve the enterprise activation AI strategy, with accountability for AI eco-system governance, platform, tool, and model activation, and end-to-end value optimization • Assess, shape the existing AI vendor, and platform landscape to identify value optimization opportunities aligned to business priorities, enterprise constraints, and security requirements • Define pragmatic governance, operating models, and guardrails that enable responsible AI adoption without creating unnecessary friction • Establish frameworks to measure usage, risk, and optimization opportunities across our enterprise landscape and our workforce • Monitor emerging AI technologies, market developments, and vendor roadmaps to inform strategic decisions and maintain competitive advantage • Partner with business leaders to enable decisioning and to connect this to enterprise IT execution • Act as a trusted advisor rather than a reactive order taker • Work within, and orchestrate across, IT and cybersecurity teams to reduce unnecessary friction while working within a risk tolerant framework appropriate to address the business needs • Design and promote pragmatic AI governance models that drive low process friction and high AI adoption • Balance speed, innovation, and security in decision-making • Drive cultural change within IT toward a consultative, business-enabling mindset • Challenge legacy control-driven behaviors that limit innovation • Influence stakeholders across multiple levels without relying solely on authority • Educate business and IT teams on AI opportunities, risks, and mitigation strategies