aCommerce - Lead AI Consultant (BPM)
Requirements
• Proven track record of designing "blueprints" or technical product specs that engineers have successfully built and deployed. • Strong stakeholder management skills; ability to explain highly complex AI concepts (like neural networks, transformers, or regression models) to non-technical business leaders in plain English. • Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, or a closely related technical field. • 5+ years of experience navigating hybrid roles such as Technical Consultant, Solutions Architect, Project Manager or Business Analyst. • Minimum 2+ years of hands-on experience specifically working on Artificial Intelligence or Machine Learning projects. • Artificial Intelligence or Machine Learning projects • Experience in engaging with Senior Management to gain consensus and sponsorship for key initiatives • Strong command of written and spoken English is required. Good command of the Thai language is a strong plus. • Deep understanding of current AI tools, generative AI technologies, agent systems, and emerging ecosystem trends. • Knowledge of data privacy, security standards, and ethical considerations surrounding corporate AI deployment. • Familiarity with the software development lifecycle to effectively collaborate with developers and technical product managers.
Responsibilities
• Internal Business Consulting: Partner directly with leadership and domain experts across Marketing, Supply Chain, Inventory, and Finance to deeply understand their workflows, identify pain points, and uncover data-driven optimization opportunities. • Internal Business Consulting: • Business & Systems Analysis: Extract, analyze, and document complex business rules, transforming vague departmental needs into highly structured functional and technical product requirements. • Business & Systems Analysis: • AI Solution Blueprinting: Design end-to-end technical blueprints and system architectures for AI products. This includes defining data pipelines, data model structures, API integrations, and model performance metrics (e.g., accuracy thresholds) so that engineering teams can build with minimal ambiguity. • AI Solution Blueprinting: • Technical Feasibility & Prototyping: Leverage your programming background to validate data availability, query databases, and build occasional quick proofs-of-concept (PoCs) to prove feasibility before handing work over to the core development team. • Technical Feasibility & Prototyping: • Product Lifecycle Management: Own the product lifecycle from initial discovery through delivery. Define the minimum viable product (MVP) scope, manage risk, and ensure the engineering output aligns perfectly with the original business intent. • Product Lifecycle Management: • Required Technical & Professional Skills • 1. Hands-On Technical Programming (Proficient Level) • Because you will be designing the technical framework for the AI development team, you must speak their language fluently. Hands-on coding testing will be part of our interview process. • Python: Proficient in data analysis, manipulating data structures, and working with core AI/ML packages. • Python: • SQL: Expert-level capability to write complex queries, joins, and aggregations across relational databases to locate and assess business data. • C++: Strong understanding of object-oriented programming, memory management, and compiled logic to architect high-performance components or system integrations. • JavaScript / TypeScript: Capable of understanding web architecture, front-end data handling, or building simple prototype UIs to demonstrate how an AI feature will look to the end-user. • JavaScript / TypeScript: • 2. Business Analysis & Domain Knowledge • Demonstrated experience translating abstract business problems into technical product documentation (PRDs, wireframes, user stories, architecture flows). • Functional familiarity with key business operations: • Supply Chain & Inventory: Forecasting, demand planning, logistics optimization. • Finance & Accounting: Fraud detection, anomaly detection, automated reconciliation.
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