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Architect, develop, and deploy production-grade LLM capabilities within the Kayzen Console. Build backend services and integrations while ensuring high performance, reliability, and observability of AI features.
Hello š I am Servesh, Co- founder and CTO at Kayzen, and I am now looking for a Senior AI Engineer who will be part of our Engineering team. š But wait, you have not heard of Kayzen before? š
Kayzen is a mobile demand-side platform (DSP) dedicated to democratizing programmatic advertising. We enable leading apps, agencies, media buyers, and brands to run programmatic customer acquisition, retargeting, and brand performance campaigns through its self-serve and managed service options. Built on the three core pillars of performance, transparency, and control, Kayzen powers the worldās best mobile marketing teams with bespoke solutions that fuel business growth and deliver a competitive advantage. With an unprecedented scale of 160B+ daily ad requests from 1.6B+ unique users worldwide, we serve up to 1B+ ads per day in 180 countries. Kayzen is accessible through our APIs and user interface.
The Team
You will work closely with our Console Engineering, Product and ML teams. Our Engineering organization builds and operates large-scale distributed systems, real-time bidding and budget systems, event and stream processing, data pipelines, and customer-facing products. For this role, the focus is practical: building AI-powered capabilities that become part of the Kayzen Console and are used in real production workflows.
Sounds interesting. Isn't it?
About the Role
We are looking for a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience shipping LLM-powered products to production. You will design and build AI features for the Kayzen Console and beyond, working across backend services, product-facing functionality, and the LLM layer.
This is a hands-on engineering role. We are not looking for a research-focused ML profile or someone who has only experimented with LLMs. This role is ideal for engineers who have already built, shipped, monitored and improved production LLM systems and is comfortable contributing in a full-stack product environment .
Responsibilities
Requirements:
Hands-on AI / LLM Experience
Practical experience with RAG, embeddings, vector search and/or agentic systems.
Strong Software Engineering / Full-Stack Skills
Product & Startup Mindset
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