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Freehand builds AI agents that replace human decision-making and coordination in enterprise software, handling analysis, negotiation, prioritization, and execution for supply chain and logistics workflows. Customers include Apple, Meta, GE, Pfizer, J&J, Schlumberger, Cardinal Health, and Unilever.
As a Backend Engineer, you will build the distributed systems, APIs, and AI agent infrastructure powering autonomous enterprise execution. This is not CRUD SaaS work—you'll build backend systems handling real money and real risk, with full observability and testing infrastructure.
Key responsibilities include: designing and scaling high-throughput, low-latency backend services for AI agent execution; building workflow orchestration systems for complex multi-step enterprise processes; implementing rule engines and policy enforcement for compliance; developing APIs and integrations with ERPs, procurement tools, payment rails, and data providers; building observability, audit, and traceability systems for AI-driven decisions; creating secure data pipelines for sensitive financial and operational data; designing agent execution runtimes and multi-agent coordination; and building evaluation and testing harnesses to validate agent behavior and reliability.
Required skills: strong production backend systems experience; proficiency in Python, NodeJS, Go, or similar languages; deep understanding of distributed systems, APIs, microservices, databases (SQL/NoSQL), and event-driven architectures; proven experience designing scalable, fault-tolerant systems; understanding of consistency, concurrency, and data integrity trade-offs; experience with financial systems, enterprise SaaS, or regulated data environments.
Bonus experience includes multi-agent orchestration, event sourcing, rule engines, production debugging at scale, open-source LLM tooling, agent observability tools (LangSmith, Arize, Helicone), and LLM-powered systems in production.
Freehand is Series C with ~100 people across the US and India. The company operates on five core principles: Context Before Confidence, Decisions not Recommendations, High Agency and High Trust, emphasizing autonomy, accountability, and visible impact.
