Senior Machine Learning Engineer - Applied AI

You will set technical direction, make the hard model and system calls, and be accountable for quality when the system is live in front of customers. This is a high-ownership role for someone who has shipped applied ML and LLM systems into production, watched them break on messy real-world data, and rebuilt them to hold.

Responsibilities

• Own the core ML and AI systems of the product and the technical direction behind them.
• Build retrieval, ranking, and LLM systems over large volumes of real operational data.
• Design LLM workflows and agentic flows that produce structured, reliable outputs and operate safely against production data.
• Build the evaluation harness and guardrails: measure grounding, consistency, and hallucination, price the cost of a wrong answer, and catch bad outputs before a user sees them.
• Own production health: latency, inference cost, output consistency, observability, and the tuning cadence that keeps quality from degrading.
• Raise the engineering bar for the team. Engineers will look to you for how to decide, not just what to do.

Requirements

• 4+ years building and shipping applied ML or LLM systems that ran in production.
• Strong Python and solid ML fundamentals.
• You understand why a system behaves the way it does, not just which function to call.
• End-to-end ownership of an ML or LLM pipeline: data, modelling or orchestration, evaluation, deployment, and the follow-through of improving quality in production.
• Hands-on production experience with LLM systems: RAG, agents, structured outputs, and disciplined prompt engineering backed by evaluation.
• Depth in retrieval: embeddings, vector search, and semantic retrieval over messy real-world data.
• Experience with ranking, recommendation, or personalisation systems, and the judgment to reason about relevance and ordering.
• Backend competence to ship the whole feature: APIs, SQL and relational databases, cloud deployment, logging, and observability.

You'll stand out if you have

• Evaluation or guardrail systems you have built for LLM outputs, especially around hallucination, grounding, and consistency.
• Breadth across the current AI stack: orchestration frameworks, vector databases, in-memory stores, streaming event processing, and LLM observability tooling.
• Memory, context management, or follow-up resolution for a conversational system at scale.
• Experience mentoring engineers and raising a team's standards.