Senior ML Engineer - Secure Production AI & LLMs

eFinancialCareers; Greater London; On-site; GBP 59,000 - 72,000; Full time

Job summary

eFinancialCareers in Central London is seeking a Senior Machine Learning Engineer to design, build and operate advanced ML solutions that directly support UK national security objectives. The role sits in a multidisciplinary AI engineering environment, collaborating with Data Scientists, Software Engineers, Product teams and government stakeholders.

The successful candidate will balance rapid experimentation with production readiness, deploying secure, production-grade ML systems on an AWS-based

Qualifications

• Commercial experience developing and deploying ML models in Python.
• Proficiency with ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Experience using AWS SageMaker, Lambda and S3.
• Strong design of experiments, including A/B testing and statistical evaluation.
• Hands-on with MLOps tools like MLflow and Weights & Biases.
• Experience building GenAI/LLM apps and prompt engineering.
• Familiarity with LangChain, LangSmith or LangGraph.

Responsibilities

• Design, develop and optimise ML models for traditional and GenAI use cases.
• Lead experimentation cycles with governance and evaluation.
• Transition experiments into production ML services with deployment monitoring.
• Build scalable pipelines using AWS and modern ML tooling.
• Implement robust experiment tracking and model versioning.
• Collaborate with cross-functional teams and mentor juniors.

Skills

Python

PyTorch

TensorFlow

SageMaker

MLflow

Weights & Biases

AB testing

LangChain

LLM

Tools

Docker

Kubernetes

Terraform

CloudFormation

Spark

Job description

eFinancialCareers in Central London is seeking a Senior Machine Learning Engineer to design, build and operate advanced ML solutions that directly support UK national security objectives. The role sits in a multidisciplinary AI engineering environment, collaborating with Data Scientists, Software Engineers, Product teams and government stakeholders.

The successful candidate will balance rapid experimentation with production readiness, deploying secure, production-grade ML systems on an AWS-based