Senior Machine Learning Engineer

James Chase; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

James Chase in the United Kingdom seeks a senior Machine Learning Engineer to design and build greenfield AI/ML platforms, including GenAI apps and agentic workflows. This is a hands-on, senior IC role focused on production ML engineering, architecture decisions and mentoring engineers without direct line management.

You will work with Python, PyTorch, MLOps, CI/CD, cloud platforms (AWS/Azure) and modern ML frameworks to deliver scalable production systems.

Qualifications

• 12+ years of experience building production AI/ML systems.
• Strong software engineering foundation with production-grade delivery.
• Experience with Generative AI, LLMs, RAG and multi-agent systems.

Responsibilities

• Architect and implement production-grade ML/AI systems, including GenAI platforms and agentic workflows.
• Lead technical decisions on ML architecture, frameworks, deployment and infrastructure.
• Develop and deploy ML models using Python and PyTorch, building scalable APIs and MLOps pipelines.
• Take AI concepts from proofs of concept through to reliable production solutions and establish engineering best practices.

Skills

Python

PyTorch

MLOps

CI/CD

Docker

Kubernetes

FastAPI

AWS

Azure

Education

BSc in Computer Science / ML / Math

Tools

Docker

Kubernetes

FastAPI

AWS

Azure

Job description

We're working with a leading global financial services organisation that is making a significant investment in AI and Machine Learning, building new AI Innovation teams to develop and deploy next-generation AI solutions.

This is an opportunity for an experienced Machine Learning Engineer to play a key technical role in designing and building greenfield AI/ML platforms, including sophisticated agentic AI systems, LLM-powered applications and intelligent automation.

We're looking for someone with a strong background in production ML engineering, rather than a predominantly research or Data Science background. You'll need to be comfortable taking complex AI solutions from initial concept and experimentation through to scalable, enterprise-grade production systems.

This is a senior, hands-on individual contributor position, with responsibility for technical architecture, engineering standards and mentoring other engineers, but no direct line management.

The Role

• Architect and develop production-grade ML/AI systems, including GenAI platforms, LLM applications and agentic workflows.
• Design and implement RAG architectures, multi-agent systems and intelligent automation solutions.
• Lead technical decisions around ML architecture, frameworks, model deployment and infrastructure.
• Develop and deploy ML models using Python, PyTorch and modern ML frameworks.
• Build scalable APIs, MLOps pipelines and cloud-native infrastructure to support production AI systems.
• Take AI concepts and proofs of concept through to reliable, scalable production solutions.
• Establish engineering best practices around testing, CI/CD, model monitoring, performance and AI governance.
• Provide technical guidance and mentorship while remaining actively involved in hands-on engineering.

What We're Looking For

• 12+ years' professional experience building production AI/ML systems, with a strong software engineering foundation.
• Extensive hands-on Python experience and strong knowledge of ML algorithms, deep learning and frameworks such as PyTorch.
• Proven experience architecting and delivering scalable ML platforms and solutions from the ground up.
• Advanced practical experience with Generative AI, LLMs, RAG, agentic AI and multi-agent systems.
• Experience with model training, fine-tuning, evaluation and production deployment.
• Strong MLOps knowledge, including Docker, Kubernetes, CI/CD, model monitoring and workflow orchestration.
• Experience with AWS or Azure, along with API development using frameworks such as FastAPI.
• Ability to make and justify complex architectural decisions, including technology selection and engineering trade-offs.
• Experience mentoring engineers and providing technical leadership without necessarily managing a team.
• A degree in Computer Science, Machine Learning, Data Science, Applied Mathematics or a related discipline.

Why Consider It?

This is an opportunity to join a well-funded AI transformation programme with significant executive backing, where you'll have genuine influence over technical direction and the opportunity to build new ML capabilities rather than simply maintain existing systems.

You’ll be working on challenging problems involving NLP, document intelligence, reasoning systems and intelligent automation, with access to modern AI infrastructure and the opportunity to influence how AI is adopted across a global organisation.

If you're an experienced, hands-on ML Engineer who enjoys designing complex systems, solving difficult engineering problems and turning emerging AI technologies into real production solutions, I'd be interested in speaking with you.