> Markdown version of [/jobs/ext/4555843-backend-engineer](https://www.wearedevelopers.com/jobs/ext/4555843-backend-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Backend Engineer - **Company:** LIGHTHOUZ AI INC. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Programming, Data Cleansing, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Node.Js, NoSQL, Queueing Systems, Regression Testing, SQL Databases, Systems Integration, Data Logging, Enterprise Software Applications, Cloud Platform System, Data Ingestion, Flask (Web Framework), Backend, Agentic-AI, Event Driven Architecture, Build Management, Kubernetes, Performance Monitor, Apache Kafka, Machine Learning Operations, Front End Software Development, Restful APIs, Wearables, Amazon Simple Queue Service (SQS), Data Pipelines, Docker, Microservices - **Published:** November 4, 2026 - **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/senior-backend-engineer-india/12066428 ## About the Role 3+ years experience building backend systems in production Strong programming skills in Python, Node.js, and Flask Deep understanding of REST APIs, microservices, and event-driven architectures Hands-on experience with PostgreSQL, MongoDB, or other SQL/NoSQL systems Comfort working in cloud environments like AWS, using Docker/Kubernetes Strong communication and collaboration skills in a remote-first team Self-starter mindset-you take ownership and move fast Nice to Have Experience in freight, logistics, fintech, or document-heavy domains Exposure to AI/ML workflows and integrating model outputs into backend systems Familiarity with message queues (e.g. SQS, Kafka) Background in building secure, enterprise-grade software Familiarity with frontend javascript tech What We Offer ## Description Build and scale backend infrastructure for AI-driven freight finance workflows, including document processing, APIs, data pipelines, audit engines, integrations, observability, and performance monitoring. Collaborate with AI/ML teams to productionize model outputs, integrate freight and financial systems, improve engineering practices, and make frontend updates as needed in a remote-first startup environment. The summary above was generated by AI About Lighthouz AI Lighthouz AI is automating the back office of freight finance with freight-native AI agents. We help freight brokers, 3PLs, and factoring companies process invoices, rate confirmations, and PoDs in seconds-not hours-by replacing manual audits and brittle RPA with intelligent automation. Our platform handles real-world document chaos-scanned and handwritten paperwork, NOAs, BOLs, emails, and portal logins-executing complex workflows automatically. The result: faster payments, fewer disputes, and 10x operational throughput. We're a Y Combinator S24 company founded by a team with deep experience across AI, supply chain, and enterprise systems (Google, Georgia Tech, Progressive, Halliburton). At Lighthouz, we're not just streamlining freight finance-we're rebuilding it from the ground up., We're looking for a Backend Engineer with familiarity with front end tech to join our fast-growing engineering team. You'll play a key role in building and scaling the core infrastructure powering our AI agents-from data ingestion and document processing to audit logic and integration pipelines. You'll work closely with our founders to ship production-grade systems that support high-volume freight workflows and power real-time financial decision-making. What You'll Do Design and build scalable backend systems to support AI-driven invoice and document processing Integrate with freight industry systems-TMSs, FMS, factoring portals, and ERPs Develop APIs, data pipelines, and audit engines that drive automation Collaborate with AI/ML teams to productionize model outputs Implement observability, performance monitoring, and logging across services Help shape engineering best practices in a fast-paced, early-stage environment Make frontend updates as necessary