We are building a vertical AI Sales OS platform designed to automate and enhance the sales process end-to-end, delivering human-level sales capability at machine speed through deep workflow integrations across telephony, messaging, CRM systems, scheduling, and reporting. As a Founding Backend Engineer, you will take ownership of the core backend systems that power real-time voice and chat interactions, campaign orchestration, lead management, reporting engines, and mission-critical integrations. This is a high-impact, high-ownership role where you will work closely with the founder on architecture, product direction, and long-term technical strategy, with meaningful ESOP upside and the opportunity to shape the foundation of a fast-scaling AI-driven SaaS platform.
Key Responsibilities
Design, build, and ship scalable backend services in Python for campaign workflows, lead orchestration, reporting, and integrations
Architect and maintain secure, well-structured REST APIs and internal microservices
Develop real-time, event-driven systems using queues, webhooks, background workers, and streaming workflows
Implement caching strategies using Redis or similar technologies to improve performance and reduce latency
Optimize database queries and ensure efficient data modeling and storage strategies
Integrate with external APIs including telephony platforms, STT/TTS providers, LLM endpoints, WhatsApp systems, and CRM tools
Implement rate limiting, retries, idempotency mechanisms, and observability best practices
Own system reliability including monitoring, logging, incident response, and deployment hygiene
Collaborate on architecture decisions, roadmap planning, and product strategy
Continuously improve performance, scalability, and cost efficiency of backend infrastructure
What Makes You a Great Fit
Strong backend engineering experience using Python
Hands-on experience with frameworks such as Django or FastAPI
Solid understanding of REST API design, authentication mechanisms, and scalable service architecture
Experience building real-time systems using event-driven architectures and message queues
Practical knowledge of caching concepts (Redis/memcache) and database fundamentals
Experience debugging performance bottlenecks and optimizing backend systems
Basic understanding of AI/ML integrations including LLM APIs, STT/TTS systems, embeddings, and model output handling
Familiarity with Docker, CI/CD pipelines, Kubernetes, and cloud platforms such as AWS or GCP
Exposure to distributed systems and message brokers like Kafka, RabbitMQ, or SQS
Comfortable working in ambiguity and taking full end-to-end ownership
Strong problem-solving mindset with the ability to build scalable, production-grade systems
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