SDE III (Full Stack)

As an SDE III (Full Stack) at Hiver, you will work end-to-end across frontend, backend, and AI systems to craft the next generation of collaborative email, automation, and AI-powered customer service workflows. You will own major product surfaces, architect scalable systems, and bring intelligent experiences to life in the UIwhile ensuring reliability for 5M+ emails processed daily across thousands of active users.

The core responsibilities for the job include the following:

Full-Stack Product Engineering:

• Build intuitive, performant UIs in React/Vue for email collaboration, analytics, and AI-assisted workflows.
• Ship features end-to-end across frontend, backend APIs, domain models, and data pipelines.
• Integrate AI suggestions, copilots, and retrieval-based workflows seamlessly into the product experience.
• Collaborate closely with design and product to deliver polished, consumer-grade UX.

Backend, Distributed Systems, and AI:

• Scale backend systems to handle rapidly growing traffic and AI workloads.
• Build low-latency inference and retrieval endpoints, caching layers, and model-serving pipelines.
• Improve reliability, resiliency, and performance across monolith and microservices environments.
• Work with Kafka/RabbitMQ to build high-volume, event-driven data flows.
• Partner with ML/AI teams to deploy embeddings, rerankers, RAG systems, and evaluation workflows.

Data, Security, and Observability:

• Implement secure, compliant data handling for structured/unstructured customer data.
• Own observability across the stack: tracing, metrics, dashboards, feature flags, and SLOs.
• Lead debugging of complex issues across frontend, backend, infra, and model pipelines.
• Drive postmortems, root-cause analysis, and process improvements.

Requirements:

• 5+ years building and scaling production systems end-to-end.
• Strong proficiency in React/Vue and TypeScript.
• Solid backend expertise in Go/Java/Python.
• Deep understanding of distributed systems, concurrency, performance, and resiliency.
• Experience with SQL plus familiarity with NoSQL/caches.
• Exposure to production model serving (embeddings, RAG, inference APIs).
• Strong fundamentals in cloud infra (AWS preferred), containers, and CI/CD.

Nice to Have:

• Experience with vector databases (pgvector, Pinecone, OpenSearch).
• Familiarity with vLLM, Triton, TorchServe, or model quantization.
• OpenTelemetry, feature flag platforms, and A/B testing systems.
• Prior experience building B2B SaaS or email/collaboration products.