Kizen is looking to solve the problem of integrating cutting-edge AI with robust backend architecture to revolutionize industries like healthcare, education, and customer experience. They aim to give every team member an AI Assistant, turning companies into AI-companies and workers into AI-Enhanced Workers.
Requirements
- 8+ years of backend engineering experience with Django, Kafka, and PostgreSQL
- 4+ years of hands-on experience building and deploying machine learning systems
- Proven track record of implementing production RAG systems at scale
- Expert-level Python development skills with Django experience
- Deep understanding of distributed systems and message queuing using message broker systems (e.g., Kafka)
- Advanced PostgreSQL knowledge, including optimization for AI workloads
- Experience building and optimizing retrieval-augmented generation (RAG) systems
Responsibilities
- Lead the design and implementation of production-ready RAG systems that integrate seamlessly with our backend infrastructure using Django, Kafka, PostgreSQL, and Clickhouse
- Architect multi-agent AI systems that operate effectively within our platform's constraints and understand business value implications.
- Design and implement sophisticated vector search solutions, including graph-based RAG systems
- Architect and build highly scalable LLM-powered systems that can handle enterprise-level workloads
- Lead LLM fine-tuning initiatives to customize models for specific business domains and use cases
- Optimize LLM performance, cost, and reliability in production environments
- Establish MLOps best practices using platforms like Langfuse or LiteLLM to ensure robust model monitoring and evaluation
Other
- Drive product strategy by providing accurate work estimations and technical roadmaps with minimal supervision.
- Mentor and develop junior engineers in AI/ML best practices
- Collaborate with cross-functional teams to translate business requirements into technical solutions
- Lead system architecture decisions and technical direction for AI initiatives
- Evaluate emerging AI technologies for potential adoption
