Opportunity :

As an AI 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 UI while ensuring reliability for 5M+ emails processed daily across thousands of active users.

Key Responsibilities :

What you will do :

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.

What we are looking for :

Must Have :

- 5+ years building and scaling production systems end-to-end.

- Solid backend expertise in Go/Java/Python.

- Strong proficiency in React/Vue and TypeScript.

- 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.