Machine Learning Engineer - LLMs & Real-Time 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

London, Vauxhall | Hybrid: at least three days in the office each week

Help build systems that turn unfolding world events into market intelligence.

At Permutable, we build AI systems that analyse global news, market and proprietary data to produce market intelligence and systematic signals for financial institutions.

Our work connects developments across energy, commodities, economic policy and geopolitics to the assets and markets they influence. Behind it are demanding engineering problems: interpreting unstructured information, evaluating model behaviour and running reliable pipelines as new data arrives.

We’re looking for a Machine Learning Engineer with strong Python skills who wants to take models from experimentation into production.

You’ll work closely with our founder and engineering team, contribute to technical decisions and see your work become part of the product. As our systems evolve towards real-time processing and more complex AI workflows, you’ll help decide how we build, evaluate and operate them.

What you’ll do

• Build production LLM pipelines. Develop systems spanning data preparation, model training and fine-tuning, evaluation, deployment and ongoing monitoring.
• Improve model performance. Use Python and our in-house GPU infrastructure to experiment with model architectures and fine-tuning approaches, assessing quality alongside latency and compute requirements.
• Make evaluation part of development. Build repeatable evaluations, investigate failure cases and use the results to guide model and pipeline improvements.
• Orchestrate data and model workflows. Build and maintain Apache Airflow pipelines, with monitoring, retries and clear approaches to failure recovery.
• Deploy and operate AI services. Work with AWS services including Lambda, ECS/EKS, S3 and Redshift, supported by Docker, GitHub Actions and Pulumi.
• Take responsibility for what you build. Follow your work through deployment, monitor its behaviour in production and resolve problems with the team.
• Contribute to engineering decisions. Bring ideas, test assumptions and help choose practical approaches to the next technical challenge.

What you’ll bring

• At least two to three years of professional AI/ML engineering experience, or equivalent experience delivering substantial, relevant systems.
• Strong Python skills and software engineering fundamentals, including maintainable code, testing, debugging and version control.
• Hands-on experience building, training or fine-tuning machine learning models, with a clear understanding of how to evaluate their performance.
• Experience taking models or AI pipelines into production and understanding what is needed to keep them working reliably.
• Familiarity with MLOps practices, including CI/CD, containerisation, automated testing and monitoring.
• A grounding in computer science, engineering, machine learning, mathematics or a related discipline, developed through study or practical experience.
• Curiosity, initiative and the ability to work through unfamiliar problems. You’re comfortable discussing trade-offs, asking questions and taking responsibility for delivery.

Useful additional experience

LLMs or NLP, GPU workloads, Apache Airflow, AWS, infrastructure-as-code tools such as Pulumi, or financial-market data.

You don’t need experience with every tool in our stack. We’re interested in what you’ve built, how you approach problems and your ability to learn.

• Work on a substantial technical challenge. Help turn large volumes of changing, unstructured information into intelligence financial institutions can use.
• Have a voice in what gets built. Work directly with our founder and engineers, contributing to model choices, evaluation methods and system design.
• Develop across the full ML lifecycle. Build experience in experimentation, GPU-based training, cloud deployment and production operations.
• See the outcome of your work. Your models and pipelines will become part of a live product, with responsibility extending beyond the initial deployment.

We work together in our Vauxhall office at least three days each week, making time to exchange ideas, solve problems and learn from one another.

If you want to help us build the systems that turn the world’s unfolding events into intelligence financial institutions can act on, we'd love to hear from you.