We are looking for a Principal Backend Architect to lead the design, architecture, and scaling of Revspot’s backend systems. This role is meant for someone who can think deeply about system design, scalability, performance, reliability, and long-term engineering decisions.
You will work closely with the CTO, engineering leaders, product, data, and AI teams to build backend systems that power Revspot’s AI-native revenue infrastructure. The ideal candidate should be hands-on, technically strong, and comfortable owning complex architecture decisions from design to execution.
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Own backend architecture and system designDesign scalable, reliable, and secure backend systems.
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Define architecture patterns, service boundaries, APIs, and data flows.
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Ensure the backend can support high-volume, real-time, and AI-driven workflows.
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Build and scale core backend platformsWork on backend services that power AI agents, lead qualification, calling workflows, automation, CRM integrations, analytics, and customer-facing systems.
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Improve system performance, reliability, observability, and fault tolerance.
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Drive technical decision-makingEvaluate and select the right technologies, frameworks, databases, infrastructure, and design patterns.
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Create clear technical design documents, architecture reviews, and implementation plans.
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Help the team make trade-offs between speed, scalability, and maintainability.
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Mentor and guide engineering teamsGuide senior and mid-level engineers on system design, backend best practices, and code quality.
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Review critical technical designs and major pull requests.
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Raise the overall engineering maturity of the backend team.
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Improve engineering standardsSet standards for API design, database design, performance, testing, monitoring, logging, deployment, and security.
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Build processes that reduce production issues and improve engineering velocity.
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Work closely with product and business teamsTranslate business requirements into scalable technical solutions.
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Partner with product and AI teams to build systems that support fast experimentation without breaking reliability.
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Own reliability and performanceIdentify system bottlenecks and improve latency, throughput, uptime, and cost efficiency.
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Drive improvements in monitoring, alerting, debugging, incident management, and post-mortems.
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8+ years of experience in backend engineering, distributed systems, or platform engineering.
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Strong experience designing and scaling backend systems in high-growth environments.
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Strong proficiency in Python / Java / Node.js / Go or similar backend languages.
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Deep understanding of system design, microservices, APIs, queues, event-driven architecture, and distributed systems.
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Strong database experience with MySQL / PostgreSQL, and NoSQL databases such as MongoDB / Redis / DynamoDB.
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Experience with cloud infrastructure, preferably AWS.
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Strong understanding of performance optimization, caching, indexing, async processing, and system reliability.
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Experience with observability tools, logging, monitoring, alerting, and incident management.
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Ability to write clean, maintainable, and production-grade code.
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Strong communication skills and ability to explain complex technical decisions clearly.
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Prior experience mentoring engineers and leading architecture discussions.
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Experience building LLM-based applications, AI agents, voice agents, or automation platforms.
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Experience with real-time systems, call systems, CRM integrations, workflow engines, or sales/marketing automation.
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Experience working in fast-paced startups.
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Experience with Kubernetes, Docker, CI/CD, Terraform, or infrastructure-as-code.
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Experience with security, compliance, and data privacy best practices.
The ideal candidate is someone who is not just a senior coder, but a technical systems thinker. They should be able to look at a business problem, break it into technical systems, design scalable architecture, and also guide the team to execute it properly.
They should be comfortable with ambiguity, fast execution, and high ownership. They should be able to balance immediate product speed with long-term engineering quality.
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Backend systems are more scalable, stable, and easier to maintain.
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Engineering teams have clearer architecture standards and better technical direction.
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Production issues reduce over time because systems are designed better.
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New product and AI-agent features can be built faster on top of stronger backend foundations.
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The engineering team becomes stronger through mentorship and better practices.
