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Freehand

Autonomous AI agents optimize enterprise spend

Backend Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

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.
  • Comfort operating in environments where backend failures have a real business impact.

Responsibilities

  • Build high-throughput, low-latency backend services that power AI agent execution.
  • Build workflow orchestration systems for complex, multi-step enterprise processes.
  • Build rule engines and policy enforcement layers for enterprise compliance.
  • Build APIs and integrations with ERP systems, procurement tools, payment rails, and data providers.
  • Build observability, audit, and traceability systems for AI-driven decisions.
  • Build secure data pipelines handling sensitive financial and operational data.
  • Build agent execution runtimes, multi-agent coordination, and observability pipelines for AI-driven workflows.
  • Build evaluation and testing harnesses to validate agent behavior, reliability, and output quality.
  • Design and build scalable backend services using modern systems 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 deeply with ERP systems, procurement platforms, payment gateways, and financial rails.
  • Handle complex data normalization and transformation across enterprise systems.
  • Build agent execution runtimes for tool calling, context management, memory, and multi-step reasoning loops.
  • Implement multi-agent coordination for parallel and sequential workflows with human-in-the-loop escalation.
  • Instrument agent execution with full trace capture, including tool calls, large language model input and output, 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; 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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