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Role Overview We are looking for an AI Engineer to build and own our intelligence platform. You will take clean, structured data delivered by the Data Analyst and build AI-powered modules that deliver actionable insights to R&D, Marketing, and other departments. Your primary ownership is from data input to insight output designing the AI architecture, building department-specific modules, and ensuring that every output is accurate, grounded, and genuinely useful for business decisions What You Will Do AI Platform Architecture Design and build a modular AI platform with department-specific intelligence branches (R&D, Marketing, Supply Chain, etc.)Select and integrate LLM APIs (Claude, GPT-4, Gemini) based on use case requirementsImplement RAG (Retrieval Augmented Generation) to ground all outputs in actual business data Data Input Specification Define exact input requirements for each AI module fields, format, quality standards, and update frequencyCollaborate with the Data Analyst to ensure data arriving in the AI layer is reliable and completeBuild validation checks to catch bad data before it enters the AI pipeline Department Intelligence Modules R&D Module: Build the White Space Finder analyzes competitor product data to surface market gaps and new product opportunitiesMarketing Module: Build the Keyword Intelligence tool interprets trend data and recommends digital marketing strategyExtend the platform to other departments over time as the business scales Output Quality & Accuracy Own the quality, reliability, and accuracy of all AI-generated insightsBuild evaluation mechanisms to detect hallucinations, vague outputs, or low-quality responsesContinuously improve prompt engineering and model configurations based on feedback Maintenance & Documentation Monitor pipelines for failures and degraded output qualityManage API usage and optimize costs across all modulesDocument all workflows, prompt strategies, and architectural decisions for future team scaling What You Will Build A fully integrated AI content engine generation, iteration, and quality control in one systemAutomated social listening and trend analysis pipelines relevant to personal carePerformance marketing agents that flag fatigue, suggest variations, and reduce manual optimisationInternal dashboards for real-time insights across sales, content, and marketing performanceA living AI tool map what we use, why, how, and what it costsA repeatable process for how we evaluate and onboard any new AI tool going forward Tools and Skills Python Must HaveLLM APIs (Claude, OpenAI, Gemini) Must HaveLangChain, LlamaIndex, or similar AI frameworks Must HaveRAG & Vector Databases (Pinecone, Chroma, FAISS) Must HavePrompt Engineering Must HaveGit / GitHub Must Havepandas, JSON, SQL basics Must HaveBuilding data scraping pipelines Added Advantage Qualifications 23 years in a technology, product, growth, AI, or startup role internships and independent projects countGraduate in Engineering, Computer Science, Business, or any discipline with strong analytical groundingCertification in AI, prompt engineering, or automation tools is a plus not a requirement Requirements Qualifications 23 years in a technology, product, growth, AI, or startup role internships and independent projects countGraduate in Engineering, Computer Science, Business, or any discipline with strong analytical groundingCertification in AI, prompt engineering, or automation tools is a plus not a requirement Role Overview We are looking for an AI Engineer to build and own our intelligence platform. You will take clean, structured data delivered by the Data Analyst and build AI-powered modules that deliver actionable insights to R&D, Marketing, and other departments. Your primary ownership is from data input to insight output designing the AI architecture, building department-specific modules, and ensuring that every output is accurate, grounded, and genuinely useful for business decisions What You Will Do AI Platform Architecture Design and build a modular AI platform with department-specific intelligence branches (R&D, Marketing, Supply Chain, etc.)Select and integrate LLM APIs (Claude, GPT-4, Gemini) based on use case requirementsImplement RAG (Retrieval Augmented Generation) to ground all outputs in actual business data Data Input Specification Define exact input requirements for each AI module fields, format, quality standards, and update frequencyCollaborate with the Data Analyst to ensure data arriving in the AI layer is relia