Vacancy description
AI Engineer in Noida, भारत
Supply chain domain Tech product company
- Full-timeStandard weekly hours
Noida, भारत
AI Engineer Location Experience Employment type Reports to Gurgaon / Noida 0-3 years Full-time **Role summary** “Advanced supply chain capability live in weeks, at a fraction of enterprise cost to serve” We are building the Supply Chain Operating System for India’s manufacturers and distributors that run their operations on homegrown ERPs but lack real-time visibility into inventory health, order flow, or logistics performance. Our system sits on top of existing ERPs and WMSs, creates a unified operational data model, and turns inventory data into better decisions through an intelligent pooling of AI agents and deterministic models. The result is a single, always-on system that gives supply chain teams enterprise-grade intelligence, without an enterprise-grade IT lift. As an AI Engineer, you will build the intelligence and conversational layer of the product: trusted answers, alerts, recommendations and workflows grounded in each customer's operational data. You will combine deterministic logic, statistical models and LLMs deliberately to solve customer’s problems. **What you will do**
- Own the evolution of the product’s agentic architecture.
- Build the intelligence that turns customer operational data into trusted answers, alerts, recommendations and
- Build conversational experiences across web and WhatsApp.
- Establish strong practices for AI evaluation, observability and continuous improvement.
- Work across the product and engineering team to determine where deterministic logic, statistical models and
- Experience in applied AI/ML engineering, graph/data systems or backend engineering for AI products.
- Strong Python and experience building APIs, data pipelines and production services.
- Hands-on experience with LLM applications: RAG/retrieval, embeddings, function/tool calling, structured
- Strong software-engineering habits: modular code, testing, version control, observability and documentation.
- Comfortable working with ambiguous problems and turning them into reliable, testable AI systems
- Experience with graph databases, LangGraph or equivalent agent orchestration frameworks, conversational
- Build AI that operates on real business data and produces decisions users can trust
- Shape the agentic and conversational experience of an operational intelligence product from its first production