AI Implementation Engineer

Role Overview:The AI Implementation Engineer will own the end-to-end AI roadmap and execution across business functions. This is a hands-on role focused on building, deploying, and scaling AI-driven solutions that enable automation, accuracy, and smarter decision-making. The role works closely with leadership to translate business priorities into high-impact, production-ready AI systems.Key Responsibilities:AI Strategy & Roadmap- Define and execute the company’s AI roadmap aligned with business goals. - Partner with cross-functional teams to identify automation and intelligence opportunities. - Drive adoption of AI for real-time decision-making and operational efficiency.Forecasting & Demand Intelligence- DD Forecasting: Build SKU-level and city-level forecasting models integrating client stock and inventory data for production, sales, and offtake planning. - D2C Forecasting: Develop self-learning AI models for SKU-level sales forecasting across channels. - Demand Sensing: Predict inter-city movement of goods using live and external data sources.Pricing, Liquidation & Revenue Optimization- Design dynamic pricing engines using automated scraping across multiple e-commerce platforms, including pincode-level optimization. - Build D2C liquidation systems triggered by real-time stock, demand, and pricing signals.Automation & Computer Vision- Implement FnV quality check automation using image-based AI for QC pass/fail decisions. - Develop auto listing engines to enable D+2 product listings using scraped images, prices, and availability data. - Create Auto PO–SO conversion systems to read purchase orders and generate corresponding sales orders.NLP, Analytics & Insights- Build customer feedback analytics using NLP across app, Amazon, and Google reviews. - Develop finance reconciliation automation to match GRN, PO, SO, POD, and payments and flag discrepancies. - Generate NPD insights by mining trends from YouTube, Google, and Amazon data. - Design influencer identification models using social and engagement analytics.Advanced Use Cases & Innovation- Implement packaging simulation models to analyze global design trends and recommend optimal packaging strategies. - Continuously improve model accuracy, scalability, and production readiness.Qualifications and Experience:- Total Experience: 2–5 years - Relevant Experience: Hands-on AI / ML / Data Science implementation - Education: B.Tech from a Tier 1 or Tier 2 Engineering College

Apply Now