Real-Time ML Engineer: LLMs & Market Intelligence

Permutable; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Job summary

Permutable in London (Vauxhall) is hiring a Machine Learning Engineer with strong Python skills to take models from experimentation into production. You’ll work with the founder and engineering team on real-time processing and complex AI workflows.

You’ll build production LLM pipelines, improve model performance, and deploy AI services using Airflow, AWS, Docker and Pulumi. The role emphasizes end-to-end ML lifecycle ownership and collaboration with engineers.

Qualifications

• Two to three years of professional AI/ML engineering experience or equivalent.
• Strong Python skills and software engineering fundamentals, including testing and version control.
• Hands-on experience building, training or fine-tuning ML models and evaluating performance.
• Experience taking models/pipelines into production with reliability considerations.
• Familiarity with MLOps, CI/CD, containerisation, automated testing and monitoring.

Responsibilities

• Build production LLM pipelines spanning data prep, training, fine-tuning, evaluation and deployment.
• Improve model performance balancing latency, compute and quality.
• Create repeatable evaluations and investigate failure cases to guide improvements.
• Orchestrate data and model workflows with Airflow pipelines and robust error handling.
• Deploy and operate AI services on AWS with Docker and CI/CD tooling.
• Contribute to engineering decisions and drive practical system design.

Skills

Python

ML engineering

MLOps

Airflow

AWS

CI/CD

GPU/Deep learning

Education

Computer Science degree or equivalent

Tools

Apache Airflow

Pulumi

Docker

GitHub Actions

Kubernetes

AWS services

Job description

Permutable in London (Vauxhall) is hiring a Machine Learning Engineer with strong Python skills to take models from experimentation into production. You’ll work with the founder and engineering team on real-time processing and complex AI workflows.

You’ll build production LLM pipelines, improve model performance, and deploy AI services using Airflow, AWS, Docker and Pulumi. The role emphasizes end-to-end ML lifecycle ownership and collaboration with engineers.