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# Technical Lead, Customs Platform

- **Company:** Klearnow Corporation
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Artificial Intelligence, Amazon Web Services, Distributed Systems, Python (Programming Language), Large Language Models, Backend, Agentic-AI, Kubernetes
- **Published:** November 12, 2026
- **Apply:** https://startup.jobs/technical-lead-customs-platform-klearnow-10755345

## About the Role

* 8+ years building backend systems, with 2+ years as a hands-on technical lead.
 * Strong backend engineering in a modern language, plus working Python.
 * Production experience with event-driven distributed systems on AWS and Kubernetes.
 * LLM or agent features shipped to real users, with evaluations used to gate releases.
 * Experience in a rules-heavy domain where errors are expensive, such as customs, tax or payments.
 * Clear writing, and comfort working with our Gurgaon team across time zones., * Some background understanding of customs brokerage.
 * Hands-on work with agentic frameworks such as Agno or LangGraph.
 * Java experience. Our backend is Java 21 with Micronaut.
 * US customs knowledge: ABI, ACE, CATAIR, HTS classification, ISF, Section 232 and 301.
 * Experience building MCP servers or similar tool interfaces for AI assistants.

## Description

You will own the customs module end to end: its architecture, its code and the team that builds it. The module takes a shipment from extracted documents to a compliant filing, covering entry, ISF, duty calculation and post-entry corrections. It is a hands-on lead role in a domain where a software mistake becomes a penalty for an importer., * Own the design of the customs module: entry data model, duty and fee engine, CATAIR validation, and ABI filing with ACE.

 * Build the brokerage agent workflow, where agents draft and check entries and a broker approves before anything is filed.
 * Keep regulated logic deterministic and tested, and use models only where inputs are ambiguous.
 * Build the evaluation sets that gate every prompt, model and rule change.
 * Make tariff and regulatory changes land as data or configuration in days.
 * Consolidate services, own the module in production, and lead, hire and mentor the team.