Define the client's AI transformation roadmap — prioritising initiatives by strategic value and execution readiness, managing board-level change, and ensuring sustainable adoption of agentic AI across the organisation.
Set architecture principles and make consequential technology choices for enterprise agentic AI programmes — spanning agent harness design, orchestration patterns, knowledge layer strategy, trust and safety frameworks, and AgentOps discipline; maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and direct senior engineers credibly.
Govern technical standards across the portfolio: orchestration and A2A patterns, knowledge layer architecture (RAG, MCP, Text-to-SQL, knowledge graphs), evaluation frameworks (golden datasets, LLM-as-judge, trajectory evals), and production readiness criteria — ensuring engineering disciplines are institutionalised, not improvised.
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Define the client's AI transformation roadmap — prioritising initiatives by strategic value and execution readiness, managing board-level change, and ensuring sustainable adoption of agentic AI across the organisation.
Set architecture principles and make consequential technology choices for enterprise agentic AI programmes — spanning agent harness design, orchestration patterns, knowledge layer strategy, trust and safety frameworks, and AgentOps discipline; maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and direct senior engineers credibly.
Govern technical standards across the portfolio: orchestration and A2A patterns, knowledge layer architecture (RAG, MCP, Text-to-SQL, knowledge graphs), evaluation frameworks (golden datasets, LLM-as-judge, trajectory evals), and production readiness criteria — ensuring engineering disciplines are institutionalised, not improvised.
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Develop a deep understanding of the business logic and merchant needs within e-commerce vertical domains, enabling Agents to accurately understand and execute highly specialized and complex business requirements, and truly solve real-world problems in merchant operations;
Continuously track cutting-edge developments in AI and large language models, and rapidly apply new technologies to product iteration.
Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science, Software Engineering, or a related discipline;
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Work closely with business stakeholders, product owners, architects, and partner teams to translate business requirements into scalable and maintainable technology solutions
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Develop and tune high-volume, low-latency data collection pipelines for time-series sensor and trace data, ensuring completeness, accuracy and traceability.
Handle protocol translation and normalization across heterogeneous tool types, vendors and generations of equipment.
Develop reusable solution components, configurable modules, plug-ins and libraries that can be templated and rolled out across multiple tool sets and fab areas.
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