Principal Machine Learning Engineer

Role Overview

We are looking for a Principal Machine Learning Engineer to join the AI Engineering team, with a primary focus on Hands-On Engineering (MLE) and MLOps Platform Reliability and GenAI Agentic Systems. This posting is at level R3 on our engineering ladder - see the level framing below for the expected scope of ownership and impact.

Level framing: Recognized technical expert; leads decisions on technical approach for projects; solves complex problems and innovates solutions; drives improvements across multiple projects/teams; may manage budgets.

Core Responsibilities

• Design and build complex ML/AI systems and services, setting the technical approach for your workstream.

• Solve complex, ambiguous technical problems that span multiple components or teams.

• Set code-quality and engineering standards for your area, and lead by example in code review.

• Own the MLOps design for a significant system: CI/CD, orchestration (Kubernetes/Prefect), and production monitoring.

• Lead root-cause analysis for complex production incidents and drive systemic fixes, not just patches.

• Extend the teams MLOps frameworks to support new model types or deployment patterns.

• Lead design of agentic AI systems (e.g. LangGraph-based), including multi-step reasoning and RAG architecture decisions.

• Own LLMOps practices for your area: evaluation pipelines, guardrails, and cost/latency optimization.

• Evaluate and introduce new GenAI tools, frameworks, or techniques where they meaningfully improve the platform.

• Mentor other engineers and help raise technical standards within your workstream.

  Key Tools Technologies

• Cloud Data Infra: AWS (EC2/ECS, S3, Lambda, IAM, CloudWatch or equivalent); Databricks Unity Catalog

• Foundational: Python; Git/GitHub; software engineering best practices; testing; SQL

• MLOps Deployment: Docker, Kubernetes, Prefect; CI/CD (GitHub Actions); production monitoring; model versioning lineage; GitOps governance

• GenAI Agentic Architecture: Claude or comparable LLMs; LangGraph or comparable agent frameworks; RAG architectures; vector databases (e.g. Pinecone); prompt engineering evaluation

  Required Qualifications

8-11 years of hands-on experience, with a track record of owning significant technical workstreams end-to-end.

• Strong proficiency in Python and a track record of writing clean, testable, production-quality code.

• Demonstrated experience owning CI/CD, containerisation, and orchestration for production ML/AI systems.

• Proven experience developing or deploying LLM-based applications, including prompt engineering, RAG, or agentic workflows.

• Strong working knowledge of containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines.

• Working knowledge of AWS cloud services and Databricks/Unity Catalog or equivalent enterprise data platforms.

• Excellent verbal and written communication skills.

• Experience working in Agile/Scrum environments.

  Education

• Bachelors or Masters degree in Computer Science, Computer Applications, or a related technical field.

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