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채용JPMorgan Chase

Software Engineer III - AI - Chase UK

JPMorgan Chase

Software Engineer III - AI - Chase UK

JPMorgan Chase

LONDON, LONDON, United Kingdom, GB

·

On-site

·

Full-time

·

6mo ago

필수 스킬

Python

AWS

PyTorch

TensorFlow

Salesforce

Machine Learning

At JP Morgan Chase, we understand that customers seek exceptional value and a seamless experience from a trusted financial institution. That's why we launched Chase UK to transform digital banking with intuitive and enjoyable customer journeys. With a strong foundation of trust established by millions of customers in the US, we have been rapidly expanding our presence in the UK and soon across Europe. We have been building the bank of the future from the ground up, offering you the chance to join us and make a significant impact.

As a Software Engineer III at JPMorgan Chase within the International Consumer Bank, you will play a crucial role in this initiative, dedicated to delivering an outstanding banking experience to our customers. You will work in a collaborative environment as part of a diverse, inclusive, and geographically distributed team. We are seeking individuals with a curious mindset and a keen interest in new technology. Our engineers are naturally solution-oriented and possess an interest in the financial sector and focus on addressing our customer needs. We work in a team focused on the delivery of a leading-edge technology stack underpinning our customer servicing capabilities.

We are seeking two talented AI Engineers to join our new AI team dedicated to developing an advanced Agent Assist platform for contact centre agents. You will design, build, and deploy machine learning models and automation solutions, collaborating with backend, Salesforce, and AWS engineers.

Key Responsibilities:

  • Design and implement AI/ML models to automate agent workflows and display tasks

  • Collaborate with backend, Salesforce, and AWS engineers to integrate AI solutions

  • Optimize model performance and scalability in production environments

  • Participate in proof-of-concept (POC) initiatives, including internal Agent assist AI tools

  • Monitor, evaluate, and improve AI-driven features based on user feedback and metrics

  • Document solutions and contribute to team knowledge sharing

  • Research and prototype innovative agent assist features (30% innovation focus)

Required Skills:

  • Proficiency in Python is mandatory

  • Strong experience in ML frameworks (Tensor Flow, Py Torch, Scikit-learn)

  • Hands-on experience with cloud platforms, especially AWS

  • Familiarity with RESTful APIs and microservices architecture

  • Experience integrating AI solutions with enterprise platforms such as Salesforce and AWS

  • Excellent problem-solving and communication skills

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Preferred Skills:

  • Experience with agent assist or contact centre technologies

  • Exposure to automation and workflow optimization

#ICBEngineering #ICBCareers

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모의 지원자 수

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스크랩

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JPMorgan Chase 소개

JPMorgan Chase

JPMorgan Chase & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.

300,000+

직원 수

New York City

본사 위치

$500B

기업 가치

리뷰

3.8

10개 리뷰

워라밸

3.2

보상

4.1

문화

3.8

커리어

3.0

경영진

2.5

65%

친구에게 추천

장점

Good benefits and compensation

Supportive and collaborative environment

Flexible work arrangements

단점

Long hours and heavy workload

Management issues and lack of direction

High stress during peak times

연봉 정보

41개 데이터

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analytics Solutions Associate

1개 리포트

$139,000

총 연봉

기본급

$107,000

주식

-

보너스

-

$139,000

$139,000

면접 경험

5개 면접

난이도

3.0

/ 5

소요 기간

14-28주

합격률

40%

경험

긍정 20%

보통 80%

부정 0%

면접 과정

1

Application Review

2

HireVue Video Interview

3

Recruiter Screen

4

Superday/Panel Interview

5

Final Interview

6

Offer

자주 나오는 질문

Behavioral/STAR

Technical Knowledge

Culture Fit

Past Experience

Case Study