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职位JPMorgan Chase

Applied AI ML Lead

JPMorgan Chase

Applied AI ML Lead

JPMorgan Chase

Hyderabad, Telangana, India, IN

·

On-site

·

Full-time

·

2w ago

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As an Applied AI ML Lead at JP Morgan Chase within Consumer and Community Banking, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job Responsibilities

  • Collaborate with cross-functional teams to identify business requirements and develop data-driven solutions using Agentic/GenAI frameworks in a fast-paced environment.
  • Conduct research on prompt and context engineering techniques to enhance the performance of LLM-based solutions.
  • Design and implement scalable and reliable data processing pipelines, performing analysis and deriving insights to optimize business outcomes.
  • Build and maintain Data Lakes and data processing workflows using Databricks to support machine learning operations.
  • Communicate technical concepts and results effectively to both technical and non-technical stakeholders.
  • Utilize AWS services including S3, Lambda, Redshift, Athena, Step Functions, MSK, EKS, and Data Lake architectures.
  • Collaborate with data scientists, engineers, and business stakeholders to deliver high-quality data solutions.
  • Act as a self-starter, independently taking initiative in driving assignments to completion and solving problems without the need for escalation.

Required Qualifications, Capabilities, and Skills

  • Advanced degree in Computer Science, Data Science, Mathematics, or a related field.
  • 5+ years of applied experience in data science, machine learning, or related areas.
  • Strong programming skills in Python, with experience in Py Spark, Spark SQL, Dataframes
  • Experience LLM Orchestration and Agentic AI libraries
  • Proficiency in using GenAI models (OpenAI or similar) to solve business problems.
  • Experience in building AI Agents, Agentic frameworks and MCP Servers.
  • Experience in building and managing data lakes and data processing workflows using Databricks.
  • Ability to leverage AI agents and tools, such as Co-Pilot, to enhance productivity and code quality.
  • Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders.
  • Strong troubleshooting and problem-solving skills.

Fast learner with the ability to quickly develop Proof of Concepts and advance them to production.

Preferred Qualifications, Capabilities, and Skills

  • Proficiency in all other AWS components—preferably AWS certified.
  • Experience integrating AI/ML models into data pipelines is a plus.
  • Experience with version control (Git) and CI/CD pipelines.
  • Experience in full stack development including Java/J2EE based microservices, React UI

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关于JPMorgan Chase

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+

员工数

New York City

总部位置

$500B

企业估值

评价

3.8

10条评价

工作生活平衡

3.2

薪酬

4.1

企业文化

3.8

职业发展

3.0

管理层

2.5

65%

推荐给朋友

优点

Good benefits and compensation

Supportive and collaborative environment

Flexible work arrangements

缺点

Long hours and heavy workload

Management issues and lack of direction

High stress during peak times

薪资范围

41个数据点

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2份报告

$188,500

年薪总额

基本工资

$145,000

股票

-

奖金

-

$182,000

$195,000

面试经验

5次面试

难度

3.0

/ 5

时长

14-28周

录用率

40%

体验

正面 20%

中性 80%

负面 0%

面试流程

1

Application Review

2

HireVue Video Interview

3

Recruiter Screen

4

Superday/Panel Interview

5

Final Interview

6

Offer

常见问题

Behavioral/STAR

Technical Knowledge

Culture Fit

Past Experience

Case Study