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💼 Full-time🗓 2026-06-25

Core

Design and implement backend systems powering AI-driven workflows (outreach, qualification, scheduling, follow-ups) and orchestrate multi-step AI agent execution.

Role type

Senior Backend Engineer (AI Infrastructure)

Builds

Scalable backend services, APIs, and orchestration layers for AI agents and enterprise integrations.

Domain

AI Agents, Enterprise Systems, SaaS

Deliverable

production ML models | infrastructure

Required skills

Backend system architecture, API design, distributed systems, data pipeline engineering, observability, LLM workflow integration, fault tolerance design, product-focused development.

Preferred skills

Experience in 0→1 environments, building systems from scratch, high ownership mindset.

Technologies

(Not explicitly stated)

Responsibilities

Design and implement backend systems for AI-driven workflows; Architect scalable services for messaging, scheduling, and CRM automation; Integrate with CRMs, calendars, and external financial platforms; Ensure system uptime, stability, and fault tolerance; Collaborate with ML engineers to productionize LLM workflows; Work with founders to define features and architecture.

Seniority

Senior, hands-on IC

Rewrite
## Responsibilities ### Core Responsibilities - Build and Scale AI Agent Infrastructure - Design and implement backend systems that power AI-driven workflows (outreach, qualification, scheduling, follow-ups) - Build orchestration layers for multi-step AI agent execution - Ensure agents can operate reliably, deterministically, and at scale ### Design Core Backend Systems - Architect scalable services for messaging, scheduling, and CRM automation - Build APIs and backend services that support both internal systems and customer-facing products - Define system boundaries, data models, and service interactions ### Integrations with Enterprise Systems - Integrate with CRMs, calendars, marketing tools, and external financial platforms - Build robust, fault-tolerant pipelines for syncing and transforming data across systems - Handle edge cases across fragmented enterprise infrastructure ### Reliability, Performance, and Observability - Ensure system uptime, stability, and fault tolerance in production - Build monitoring, logging, and alerting systems - Identify and eliminate bottlenecks in distributed workflows ### AI System Support (Cross-functional with ML) - Collaborate with ML engineers to productionize LLM workflows and agent logic - Implement guardrails, validation layers, and fallback systems for AI outputs - Support evaluation pipelines for measuring AI performance and reliability ### Product-Focused Engineering - Work directly with founders and product to define features and architecture - Turn ambiguous requirements into clear, scalable technical systems - Ship quickly and iterate based on real customer feedback ## What Success Looks Like - You build systems that allow AI agents to reliably execute complex workflows end-to-end - You reduce manual effort for advisors by automating real operational work - You help turn a fast-moving prototype into a stable, scalable enterprise-grade product - You operate with high ownership and can independently drive large parts of the backend stack This role is ideal for someone who thrives in 0→1 environments, enjoys building systems from scratch, and wants to have outsized impact in a small, high-agency team.
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