Senior Applied AI Engineer

Responsibilities

• Design, build and ship customer-facing AI features end-to-end, including RAG system design (chunking, embedding selection, retrieval, re-ranking) and agentic workflows.
• Ship external customer-facing LLM-based features into production, with safe production deployment practices.
• Do hands-on evaluation-pipeline and observability work: drift detection, quality monitoring.
• Reason independently about retrieval architecture using strong data fluency.
• Apply real engineering rigor to the Context Development Lifecycle (CDLC): generate, evaluate, distribute and observe the context that powers AI agents.
• Multiply the output and quality of the squad you join, and share patterns/practice beyond your immediate team so adoption compounds across the organisation.
• Embed directly into a client squad as a hands-on Individual Contributor.
• Requirements:
• Python (Python-first).
• RAG system design: chunking, embedding selection, retrieval, re-ranking.
• Agentic workflows; safe production deployment.
• Proven experience shipping external customer-facing LLM-based features to production (not prototypes).
• Hands-on evaluation-pipeline and observability work: drift detection, quality monitoring.
• Data fluency to reason about retrieval architecture independently.
• Agent & harness engineering fluency — designing guardrails, context and verification layers for safe, reliable AI-assisted delivery; concrete evidence of having built/operated this kind of tooling, not just used it.
• Comfortable with the client's current daily tools: Claude Code and GitHub Copilot.

Nice-to-Have: