Senior Applied AI Lead — Production ML & Gen AI

JPMorgan Chase & Co.

Greater London

On-site

GBP 110,000 - 150,000

Full time

43 hours ago
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Job summary

JPMorgan Chase & Co. in London is seeking an Applied AI Scientist to build ML and generative AI solutions that scale from experiments to production.

You will collaborate with product owners, data engineers, software engineers, and ML practitioners to design, test, and deploy capabilities at scale. This hands-on role rewards solving hard problems, writing robust code, and turning ideas into reliable systems.

Qualifications

  • Minimum five years of applied ML experience, including production deployments.
  • Strong ML/DL knowledge with method selection and evaluation.
  • Strong Python skills and tested software development.
  • Hands-on experience with LLM-based systems, retrieval, or agents.
  • Ability to turn unclear requirements into testable problems.
  • A STEM degree or equivalent practical experience.

Responsibilities

  • Define problems, success measures, and technical approaches with product and engineering teams.
  • Design experiments, establish baselines, and benchmark models and agent systems.
  • Own Python development from experimental prototypes to tested production code.
  • Make and explain trade-offs between model quality, reliability, latency, and cost.
  • Lead technical design reviews and mentor other ML practitioners.
  • Work with engineering and control teams to meet data, model governance, and security requirements.

Skills

Applied ML
Python programming
LLM systems
Experiment design
Independent work

Education

STEM degree or equivalent

Tools

PyTorch
TensorFlow
AWS SageMaker
AWS EKS

Job description

JPMorgan Chase & Co. in London is seeking an Applied AI Scientist to build ML and generative AI solutions that scale from experiments to production.

You will collaborate with product owners, data engineers, software engineers, and ML practitioners to design, test, and deploy capabilities at scale. This hands-on role rewards solving hard problems, writing robust code, and turning ideas into reliable systems.

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