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JPMorgan Chase
JPMorgan Chase

Global financial services firm

Applied AI/ML Lead - Intelligent Cloud Migration

직무머신러닝
경력리드급
위치London, United Kingdom
근무오피스 출근
고용정규직
게시1주 전
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Take a technical leadership position within JPMorgan's Infrastructure Platform, where you'll harness cutting-edge AI techniques to revolutionize business decisions and workflow for cloud migration.

  • As an Applied AI / ML Lead
  • Vice President
  • Machine Learning Engineer at JPMorgan Infrastructure Platform, you will be at the forefront of combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services, leveraging the latest research from fields of Generative AI, Agentic AI, and statistical machine learning to revolutionize cloud migration. You will be instrumental in building products that automate processes, help experts prioritize their time, and make better decisions. We have a growing portfolio of AI-powered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets. The role is initially that of an individual contributor.

Job responsibilities:

  • Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions.
  • Design and execute enterprise-wide, reusable AI/ML frameworks and core infrastructure to accelerate AI solution development.
  • Design and develop multi-agent systems for orchestration, agent-to-agent communication, eval, memory, telemetry, and guardrails.
  • Apply context and prompt engineering techniques to improve prompt-based model performance.
  • Develop and maintain tools and frameworks for prompt-based agent evaluation, monitoring, and optimization at enterprise scale.
  • Build and maintain data pipelines and processing workflows for scalable, efficient data consumption.
  • Write secure, high-quality production code and conduct code reviews.
  • Partner with Engineering, Product, and Business teams to identify requirements and develop solutions.
  • Communicate technical concepts and results to both technical and non-technical stakeholders, including senior leadership.
  • Provide technical leadership, mentorship, and guidance to junior engineers, promoting a culture of excellence and continuous learning.

Required qualifications, capabilities, and skills:

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
  • Experience in machine learning engineering.
  • Strong proficiency in Python and experience deploying end-to-end pipelines on AWS.
  • Hands-on experience in system design, application development, testing, and operational stability.

Preferred qualifications, capabilities, and skills:

  • Strategic thinker with the ability to drive technical vision for business impact.
  • Demonstrated leadership working with engineers, data scientists, and ML practitioners.
  • Experience with AWS and infrastructure-as-code tools such as Terraform.
  • Experience in multi-agent orchestration.
  • Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.
  • Experience with agentic telemetry and evaluation services.
  • Experience in customising and optimizing Github Copilot using VSCode Extension or Model Context Protocol (MCP)

ABOUT US

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

ABOUT THE TEAM

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

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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

10개 리뷰

3.8

10개 리뷰

워라밸

3.5

보상

4.0

문화

3.8

커리어

3.2

경영진

2.8

68%

지인 추천률

장점

Good benefits and compensation

Supportive colleagues and environment

Flexible work arrangements

단점

Long hours and heavy workload

Management issues and lack of direction

High stress and expectations

연봉 정보

44개 데이터

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2개 리포트

$188,500

총 연봉

기본급

$145,000

주식

-

보너스

-

$182,000

$195,000

면접 후기

후기 4개

난이도

3.0

/ 5

소요 기간

14-28주

합격률

50%

경험

긍정 25%

보통 75%

부정 0%

면접 과정

1

Application Review

2

HR Screen

3

Hiring Manager Interview

4

In-person/Final Interview

5

Offer

자주 나오는 질문

Behavioral/STAR

Past Experience

Culture Fit

Financial Knowledge

Case Study