Principal AI Engineer – Platform Team

About the role

We are a fast‑growing Series A startup building a unified marketing intelligence platform for enterprise customers such as Spotify, Asus and Colgate. As a Principal AI Engineer on the Platform team you will define the technical direction for AI‑assisted engineering and own the core data‑pipeline architecture that powers more than 500 integrations.

Key responsibilities

• Design, evolve and scale the Python‑based extraction, load and pipeline infrastructure serving 500+ integrations.
• Identify bottlenecks and drive performance, reliability and security improvements across Kubernetes, distributed systems, ClickHouse and PostgreSQL.
• Modernise legacy subsystems (LES, DSAS, ULS) into AI‑native, scalable components.
• Define and implement agentic development frameworks that integrate LLM tooling (e.g., Claude Code) into the delivery process with proper validation, testing and guardrails.
• Make and document critical architectural decisions, set standards for code quality, observability and incident response.
• Mentor engineers on AI‑assisted development practices to increase velocity while managing risk.
• Collaborate with product and AI teams to roll out new integration capabilities and agentic frameworks.

Required profile

• 7+ years of backend engineering experience with at least 2 years in a principal or architect role.
• Proven track record designing and scaling complex, production‑grade systems.
• Hands‑on experience using AI‑assisted development tools and building agentic code‑maintenance pipelines.
• Strong background in distributed systems, Kubernetes, workflow orchestration and message queues.
• Experience building secure, highly‑available services with robust observability and incident‑response practices.
• Comfortable working in a fast‑moving, ambiguous startup environment and within the EST timezone.

Required skills

• Python
• Kubernetes
• ClickHouse
• PostgreSQL
• Temporal (or similar workflow orchestration)
• RabbitMQ (or similar message queue)
• SQL query optimisation
• Observability and alerting tools
• AI‑assisted development (e.g., Claude Code, agentic coding)