Backend Engineer

About Freehand Freehand is building the future of enterprise operations—AI-native teams that replace traditional software and staff at the world's largest brands. Our agentic platform automates complex spend management workflows for global leaders like Unilever, Apple, Cardinal Health, J&J, Dunkin', and Meta. We are now scaling our AI Teams for Spend Management—to autonomously manage the procure-to-pay lifecycle, including PO validation, contract compliance, invoice approvals, dispute resolution, and cross-border payments. Role Overview As a Backend Engineer at Freehand, you will build the distributed systems, APIs, and AI agent infrastructure that power AI-driven enterprise execution. You will design and scale backend services that orchestrate agents, enforce business rules, integrate deeply with ERPs and financial systems, and ensure that every action taken by AI is secure, traceable, and reliable. This is not CRUD SaaS work. You will be building the backend for autonomous systems handling real money and real risk—and the observability and testing infrastructure to keep those systems trustworthy at scale. What You Will Build •High-throughput, low-latency backend services that power AI agent execution •Workflow orchestration systems for complex, multi-step enterprise processes •Rule engines and policy enforcement layers for enterprise compliance •APIs and integrations with ERPs, procurement tools, payment rails, and data providers •Observability, audit, and traceability systems for AI-driven decisions •Secure data pipelines handling sensitive financial and operational data •Agent execution runtimes, multi-agent coordination, and observability pipelines for AI-driven workflows •Evaluation and testing harnesses to validate agent behaviour, reliability, and output quality Key

Responsibilities Backend & Systems Engineering •Design and build scalable backend services using modern system design principles •Own critical services that manage procurement, invoice, contract, and payment workflows •Implement orchestration logic that coordinates AI agents, humans, and enterprise systems •Build resilient systems with strong guarantees around idempotency, retries, and failure handling API & Integration Development •Build and maintain robust APIs consumed by AI services, frontend apps, and external systems •Integrate deeply with ERP systems, procurement platforms, and payment gateways and financial rails •Handle complex data normalisation and transformation across enterprise systems AI Agent Building, Observability & Testing •Build agent execution runtimes: tool calling, context management, memory, and multi-step reasoning loops •Implement multi-agent coordination—parallel and sequential workflows with human-in-the-loop escalation •Instrument agent execution with full trace capture: tool calls, LLM I/O, latency, token usage, and cost •Design evaluation frameworks for agent output quality, task success rate, and regression detection •Build automated test harnesses for agent pipelines—unit, integration, and replay-based regression tests •Define and track agent reliability metrics: task completion rate, escalation rate, cost per workflow, and SLA adherence Reliability, Security & Compliance •Design systems with enterprise-grade reliability, monitoring, and alerting •Implement fine-grained access controls, audit logs, and data security best practices •Ensure backend systems meet compliance