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Freehand

Autonomous AI agents optimize enterprise spend

Backend Engineer

Backend EngineeringUpdated on Nov 11
Full-time$150k - $300k /yr

About the Job

Requirements

  • Six to fifteen years of experience building production backend systems.
  • Strong proficiency in Python, Node.js, Go, or a similar backend language.
  • Deep understanding of distributed systems, APIs and microservices, SQL and NoSQL databases, and message queues or event-driven architectures.
  • Proven experience designing scalable, fault-tolerant systems.
  • Strong grasp of consistency, concurrency, and data-integrity trade-offs.
  • Experience working with financial systems, enterprise SaaS platforms, or sensitive or regulated data.
  • Ability to operate in environments where backend failures have a real business impact.

Responsibilities

  • 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.
  • Build and maintain robust APIs consumed by AI services, frontend applications, and external systems.
  • Integrate with ERP systems, procurement platforms, payment gateways, and financial rails.
  • Handle complex data normalization and transformation across enterprise systems.
  • Build agent-execution runtimes, including tool calling, context management, memory, and multi-step reasoning loops.
  • Implement multi-agent coordination for parallel and sequential workflows, including human-in-the-loop escalation.
  • Instrument agent execution with trace capture for tool calls, large language model inputs and outputs, 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, including unit, integration, and replay-based regression tests.
  • Define and track agent reliability metrics, including task completion rate, escalation rate, cost per workflow, and service-level-agreement adherence.
  • 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 requirements for financial and regulated data.
  • Optimize backend systems for latency, throughput, and cost efficiency.
  • Design services that scale across global customers and billions of transactions.
  • Proactively identify and fix bottlenecks in distributed systems.

Desired Qualifications

  • Experience building workflow engines or state-driven systems.
  • Experience with multi-agent orchestration and agent-to-agent communication protocols.
  • Familiarity with event sourcing, rule engines, or policy systems.
  • Knowledge of production debugging at scale and observability tooling.
  • Contributions to or experience with open-source large language model tooling ecosystems.
  • Experience with agent-observability tooling such as LangSmith, Arize, Helicone, or custom trace pipelines, and evaluation design.
  • Experience building large-language-model-powered systems in production and familiarity with agent frameworks such as LangChain, CrewAI, or custom frameworks.

About the Company

Freehand

Autonomous AI agents optimize enterprise spend

Freehand provides autonomous AI Teams that operate inside enterprise ERP, procurement, and finance systems to manage and govern complex supply chain spend for large corporations. It reads unstructured data, reasons over structured data, and executes decisions to automate non-standard spend, invoice matching, exceptions, and vendor negotiations, effectively replacing many human roles. The platform uses a proprietary context graph to coordinate decisions across logistics, raw materials, and labor, differentiating itself by delivering fully autonomous agents that work inside existing systems rather than standalone tools. Its goal is to cut operating costs and reduce procure-to-pay and accounts payable cycle times at scale, leveraging billions in processed payments and strong client results.

Company size
51-200
Company stage
Early VC
Total funding
$75M
Headquarters
Chicago, IL
Founded
2026

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