Lead AI/ML Platform Engineer (Python, AWS, Kubernetes)

Testing Site - JPMC; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Testing Site - JPMC is seeking a Lead Software Engineer to join the Applied Artificial Intelligence and Machine Learning team within Corporate and Investment Banking. You will build robust APIs, services, and libraries while collaborating with Data Scientists to integrate AI/ML into banking systems.

You will architect scalable cloud infrastructure on AWS with Kubernetes, promote MLOps practices, and mentor junior engineers to drive automation and reliability across production systems.

Qualifications

• Formal training or certification in Computer Science, Engineering, or a related field, along with strong advanced experience in key concepts.
• Proven hands-on experience with Python and Kubernetes or ECS.
• Experience with cloud platforms such as AWS.

Responsibilities

• Develop and maintain high-quality applications using Python.
• Architect scalable and resilient cloud infrastructure solutions using AWS/Kubernetes/EKS/ECS.
• Design and deploy solutions with MLOps best practices.
• Collaborate with AI experts and internal teams to understand and integrate AI/ML with existing systems.
• Mentor and guide junior team members, lead initiatives to promote best practices and automation.
• Collaborate closely with SRE and production monitoring teams to ensure system reliability and performance.

Skills

Python

AWS

MLOps

Education

Formal training in Computer Science or related field

Tools

Kubernetes

ECS

Terraform

Job description

Testing Site - JPMC is seeking a Lead Software Engineer to join the Applied Artificial Intelligence and Machine Learning team within Corporate and Investment Banking. You will build robust APIs, services, and libraries while collaborating with Data Scientists to integrate AI/ML into banking systems.

You will architect scalable cloud infrastructure on AWS with Kubernetes, promote MLOps practices, and mentor junior engineers to drive automation and reliability across production systems.