We're building an agentic AI platform that helps enterprises transform complex business workflows into AI-powered operations. We work with leading global companies to identify high-value workflows, deploy AI agents into real business environments, and help organizations move from experimentation to measurable enterprise impact.We believe AI adoption inside the enterprise isn't just a technology challenge. It requires business context, workflow understanding, change management, trust, ownership, and a clear path to value. That's where this role comes in. We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer specializing in infrastructure to join our founding team. The focus of this role is to build and scale the infrastructure that powers autonomous AI agents automating complex enterprise workflows. You will own the systems, pipelines, and platforms that let our AI agents run reliably, securely, and at scale in production.Your ImpactInfrastructure & PlatformDesign, build, and own the core infrastructure powering our AI agent platform, from data pipelines to production deployment systems.Build and scale the backend systems that support high-throughput document processing and data extraction workloads.Cloud Infrastructure and ScalabilityArchitect and deploy infrastructure on cloud platforms (AWS, GCP, or Azure) with a focus on scalability, reliability, and cost efficiency.Own containerization and orchestration (Docker, Kubernetes) for all production workloads.Build and maintain CI/CD pipelines and DevOps practices that let the team ship fast without breaking things.Data InfrastructureDesign and manage data pipelines to process and analyze large volumes of documents and unstructured data at scale.Build the infrastructure layer connecting AI agents to databases, vector stores, and enterprise systems (ERP, CRM).API & Systems IntegrationBuild and maintain robust, well-documented APIs connecting AI agents with external systems and enterprise software.Design for reliability: retries, observability, and graceful degradation across distributed systems.Security and ComplianceImplement authentication and authorization mechanisms (OAuth2, JWT) to secure AI-driven systems.Ensure compliance with data privacy standards (e.g. GDPR, HIPAA) and drive best practices for secure data handling across the infrastructure.Monitoring and OptimizationBuild observability and monitoring systems to track infrastructure health, performance, and cost.Continuously optimize system performance for speed, reliability, and cost-efficiency at scale.CollaborationWork closely with AI/ML engineers, product, and the founding team to make sure infrastructure decisions support fast iteration and production-grade reliability.Participate in code reviews, design discussions, and architecture planning to drive infrastructure strategy.