AI platform engineer for recommendation systems

We are hiring a Founding AI Platform Engineer to own the systems that make the company's ML and GenAI products reliable, deployable, observable, and scalable. This role sits at the intersection of backend engineering, infrastructure, MLOps, and product delivery. You will build the production layer around training, evaluation, deployment, serving, CI/CD, experimentation, and monitoring.

• Build and maintain the infrastructure and tooling used to train, evaluate, deploy, and monitor ML models and GenAI services
• Own production services, APIs, and pipelines that power recommendations, agent workflows, and customer-facing integrations
• Improve CI/CD, testing, release workflows, rollback processes, and environment management
• Establish observability across service health, model behaviour, agent quality, latency, cost, and failure modes
• Build reproducibility and lifecycle practices for models, prompts, datasets, configurations, and releases
• Support experimentation and measurement infrastructure so product and ML changes can be evaluated cleanly
• Improve reliability, scalability, security, performance, and cost efficiency across the stack
• Troubleshoot production issues end-to-end and turn recurring pain points into durable engineering improvements
• Help define the platform and engineering standards the company will rely on as it grows

• Strong software engineering background with experience building and operating production systems
• Experience with backend services, cloud infrastructure, CI/CD, testing, observability, and automation
• Strong Python skills and comfort working across services, tooling, infrastructure, and operational workflows
• Good judgment about reliability, performance, maintainability, and cost tradeoffs
• Ability to collaborate closely with ML and product teams and move ambiguous work to completion
• High ownership, attention to detail, and a bias toward simplifying and strengthening systems
• Будет плюсом: Experience with MLOps workflows for model training, evaluation, deployment, and monitoring; Experience serving ML models or LLM applications in production; Experience with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms; Experience with agent evaluation, prompt versioning, retrieval/search infrastructure, or vector-backed systems; Experience supporting customer-facing APIs or SaaS platform infrastructure

• Direct access to the founders and real influence over the platform and engineering direction
• High-ownership role with room to shape the production foundations of the company from the beginning
• Opportunity to work on the systems behind recommendation engines and GenAI products that customers actually use
• Flexible remote environment with strong overlap with European time zones preferred
• Small team, low bureaucracy, and a lot of room to grow