AI Engineer

Role OverviewWe 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 decisionsWhat You Will DoAI 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