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
· 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
· 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