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2 weeks ago
We are seeking a seasoned expert in conversational AI to design and develop a cutting-edge platform.
The ideal candidate will have expertise in architecting scalable systems that integrate multiple LLM providers, support real-time interactions, handle complex state management, and ensure performance, observability, and security across the stack.
Key Responsibilities:- Implement advanced chat session management, including context persistence, token optimization, and retrieval-augmented generation (RAG).
- Design and optimize high-throughput APIs (REST, GraphQL, and WebSockets/SSE) with rate limiting and fault tolerance.
- Integrate token metering, analytics, and usage-based billing systems.
- Develop a secure, multi-tenant user management system with granular authentication/authorization.
- Leverage event-driven architectures (Kafka, Pub/Sub, or equivalent) for real-time processing and monitoring.
- Optimize database schemas and queries (PostgreSQL, pgvector, Supabase) for low-latency chat history retrieval.
- Implement vector search and RAG pipelines using Pinecone, Weaviate, or pgvector for knowledge grounding.
- Ensure cloud-native scalability with Docker/Kubernetes, CI/CD pipelines, and IaC (Terraform, Pulumi).
- Set up observability (distributed tracing, structured logging, metrics, error tracking) for debugging and performance monitoring.
- Apply AI safety and guardrails (moderation APIs, prompt filtering, structured outputs).
- Stay ahead of AI ecosystem developments and propose new integrations.
- 5–7 years of professional experience in full-stack or platform engineering.
- Proven experience delivering production-grade distributed systems.
- Strong understanding of LLM APIs and AI/ML system integrations.
- Bachelor's/Master's in Computer Science or equivalent practical experience from a pedigree background.
This is an excellent opportunity for professionals looking to excel in the field of conversational AI.
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