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Lead Software Engineer – Python, AIML, Cloud

JPMorgan Chase

Lead Software Engineer – Python, AIML, Cloud

JPMorgan Chase

LONDON, LONDON, United Kingdom, GB

·

On-site

·

Full-time

·

1mo ago

J.P.Morgan Chase is seeking a Lead Software Engineer with expertise in AWS and Python, and a passion for Machine Learning, to help engineer and deploy innovative ML solutions into production. You will collaborate with the Applied AI/ML group and technology teams across the firm, contributing to both new and ongoing projects.

In this role, you will work alongside Data Scientists to build cloud-based frameworks for hosting machine learning models, providing software engineering expertise throughout the model development lifecycle. You will leverage both internal and external cloud platforms, utilizing proprietary and open-source tools to ensure models meet SDLC standards, are production-ready, and can be deployed efficiently. The position requires close interaction with platform developers, engineering communities, and the integration of existing and new technologies.

Job Responsibilities

  • Develop and maintain high-quality, secure applications using Python and AWS
  • Create architecture and design deliverables, lead design and architecture reviews, promote best practice
  • Integrate AIML solutions into complex, domain-specific operations processing systems
  • Lead code reviews, design discussions, and agile planning sessions
  • Collaborate with SRE and production monitoring teams to ensure system reliability and performance
  • Contribute to software engineering communities of practice and technology events
  • Embrace continuous learning, creative problem-solving, and a can-do attitude

Required Qualifications, Capabilities, and Skills

  • Bachelor’s degree or higher in Computer Science, Engineering, or a related field, or equivalent formal training/certification
  • Proven hands-on experience in Python application development
  • Proven hands-on experience developing, debugging and maintaining production applications
  • Solid understanding of software development best practices, including version control, testing, and CI/CD
  • Strong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholders
  • Experience working on AIML systems and/or prior experience collaborating with data scientists
  • Track record of designing, building, and delivering maintainable, extensible applications into production environments

Preferred Qualifications, Capabilities, and Skills

  • Experience with Cloud services, Infrastructure as Code (IaC, Terraform) and containerized application development
  • Familiarity with data storage systems (e.g., Postgres, Open Search) and AWS services such as S3, Sage Maker, and Bedrock
  • Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps, Large Language Model Operations (LLMOps), Event Driven Systems.

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About JPMorgan Chase

JPMorgan Chase

JPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.

300,000+

Employees

New York City

Headquarters

Reviews

4.2

10 reviews

Work Life Balance

4.2

Compensation

4.3

Culture

4.5

Career

4.4

Management

4.1

75%

Recommend to a Friend

Pros

Good pay and benefits

Work-life balance

Career advancement opportunities

Cons

Heavy workload at times

Career advancement takes time

Pay could be better in some roles

Salary Ranges

47 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analyst

21 reports

$126,500

total / year

Base

$110,000

Stock

-

Bonus

-

$95,450

$155,250

Interview Experience

4 interviews

Difficulty

2.8

/ 5

Duration

14-28 weeks

Interview Process

1

Application Review

2

HireVue Video Interview

3

Technical/Behavioral Assessment

4

Final Interview Round

5

Offer Decision

Common Questions

Behavioral/STAR

Technical Knowledge

Past Experience

Culture Fit

Case Study