Senior AI Engineer (m/f/d)

Hello 👋 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. 🙌 But wait, you have not heard of Kayzen before? 😃

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 world’s 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.

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? 

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 .

• →Architect, develop, and deploy production-grade LLM capabilities within the Kayzen Console
• →Build backend services, APIs, and integrations that connect AI capabilities with our product.
• →Contribute to product-facing and full-stack functionality where needed.
• →Implement production patterns for LLMs, RAG, embeddings, agents, and tool calling.
• →Build reusable components and abstractions where they improve engineering velocity and consistency.
• →Set up and improve evaluation, observability, monitoring, and feedback loops for AI features.
• →Monitor and optimize quality, latency, reliability, and inference cost.
• →Troubleshoot production issues and iterate based on telemetry and user feedback.
• →Work closely with Product, ML and Engineering to translate product problems into pragmatic AI solutions.
• →Prototype quickly, validate ideas, and turn successful experiments into maintainable production systems.

• Experience with LLM APIs and orchestration patterns; specific frameworks are less important than strong fundamentals.
• Good understanding of production LLM concerns such as evaluation, observability, failure modes, cost and latency.
• Experience iterating on AI features based on telemetry, evaluation results or user feedback.

• Full-stack product development using Angular, JavaScript/TypeScript, Python, NodeJS etc. is a strong plus.
• Strong experience designing APIs, services and production-grade systems.
• Ability to write clean, maintainable code and take ownership from implementation through production.
• Experience contributing across a product stack and willingness to work on both backend systems and user-facing product functionality.
• Comfort working with AWS cloud infrastructure, CI/CD and production environments.

• Bias for shipping, iteration and pragmatic problem solving.
• Comfortable with ambiguity and rapid change.
• Able to collaborate closely with Product, ML and Engineering stakeholders.
• Can discuss trade-offs, challenge requirements constructively and translate AI complexity into practical product solutions.
• Thinks in terms of maintainability and reuse rather than one-off demos.

• Heavy model training or research.
• Designing custom ML algorithms.
• Academic optimization work.
• Building proofs of concept that never reach production.

• Experience with multi-agent systems, tool calling or MCP.
• Experience setting up guardrails, moderation and safety checks.
• Experience with prompt/context management, routing, caching or fallback strategies.
• Experience with Docker and cloud infrastructure (AWS, GCP or Azure).
• Experience with modern frontend development and customer-facing web applications.