AI Engineer

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

• 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

requirements

• Implement 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 frequency

• for each AI module — fields, format, quality standards, and update frequency
• Collaborate with the Data Analyst to ensure data arriving in the AI layer is reliable and complete
• Build 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 opportunities
• Marketing Module: Build the Keyword Intelligence tool — interprets trend data and recommends digital marketing strategy
• Extend the platform to other departments over time as the business scales Output Quality & Accuracy
• Own the quality, reliability, and accuracy of all AI-generated insights
• Build evaluation mechanisms to detect hallucinations, vague outputs, or low-quality responses
• Continuously improve prompt engineering and model configurations based on feedback Maintenance & Documentation
• Monitor pipelines for failures and degraded output quality
• Manage API usage and optimize costs across all modules
• Document 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 system
• Automated social listening and trend analysis pipelines relevant to personal care
• Performance marketing agents that flag fatigue, suggest variations, and reduce manual optimisation
• Internal dashboards for real-time insights across sales, content, and marketing performance
• A living AI tool map — what we use, why, how, and what it costs
• A repeatable process for how we evaluate and onboard any new AI tool going forward Tools and

Skills

• Python — Must Have
• LLM APIs (Claude, OpenAI, Gemini) — Must Have
• LangChain, LlamaIndex, or similar AI frameworks — Must Have
• RAG & Vector Databases (Pinecone, Chroma, FAISS) — Must Have
• Prompt Engineering — Must Have
• Git / GitHub — Must Have
• pandas, JSON, SQL basics — Must Have
• Building data scraping pipelines — Added Advantage

Qualifications

• 2–3 years in a technology, product, growth, AI, or startup role — internships and independent projects count
• Graduate in Engineering, Computer Science, Business, or any discipline with strong analytical grounding
• Certification in AI, prompt engineering, or automation tools is a plus — not a requirement

Requirements

Qualifications

• 2–3 years in a technology, product, growth, AI, or startup role — internships and independent projects count
• Graduate in Engineering, Computer Science, Business, or any discipline with strong analytical grounding
• Certification in AI, prompt engineering, or automation tools is a plus — not a requirement

• Implement 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 frequency

• for each AI module — fields, format, quality standards, and update frequency
• Collaborate with the Data Analyst to ensure data arriving in the AI layer is reliable and complete
• Build 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 opportunities
• Marketing Module: Build the Keyword Intelligence tool — interprets trend data and recommends digital marketing strategy
• Extend the platform to other departments over time as the business scales Output Quality & Accuracy
• Own the quality, reliability, and accuracy of all AI-generated insights
• Build evaluation mechanisms to detect hallucinations, vague outputs, or low-quality responses
• Continuously improve prompt engineering and model configurations based on feedback Maintenance & Documentation
• Monitor pipelines for failures and degraded output quality
• Manage API usage and optimize costs across all modules
• Document 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 system
• Automated social listening and trend analysis pipelines relevant to personal care
• Performance marketing agents that flag fatigue, suggest variations, and reduce manual optimisation
• Internal dashboards for real-time insights across sales, content, and marketing performance
• A living AI tool map — what we use, why,