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채용JPMorgan Chase

Applied AI ML Associate Senior- Python/Java , AWS

JPMorgan Chase

Applied AI ML Associate Senior- Python/Java , AWS

JPMorgan Chase

Bengaluru, Karnataka, India, IN

·

On-site

·

Full-time

·

4d ago

Seeking AI/ML technical leader to drive innovative banking solutions and advance analytics in financial services.

As an Applied AI ML Associate Sr within Global Bank Technology team , you will drive innovation by leading cutting-edge GenAI applications, such as search tools, chatbots, and AI agents to solve complex challenges in Global bank financial services. Collaborate with a forward-thinking team to enhance global business operations and technology solutions.

Job Responsibilities

  • Contribute towards end-to-end development and deployment of GenAI-powered applications (Search, Chatbots, Agents).
  • Leverage expertise in AI, NLP, LLM and deep learning to improve business outcomes and processes.
  • Design enterprise-level solutions for LLM and GenAI use cases on cloud platforms like AWS/Azure.
  • Drive development, testing, deployment, monitoring, and continuous operations for cloud-based high-performant, high-volume applications.
  • Implement innovative software solutions and troubleshoot complex problems beyond routine methodologies.
  • Utilize tools like Terraform IaC, Splunk, Dynatrace, Grafana, Prometheus and Datadog to build scalable and maintainable systems.
  • Optimize system architecture, operational stability, coding hygiene, and troubleshooting workflows.
  • Advance production engineering and automation practices.
  • Contribute to engineering communities, fostering a culture of innovation, diversity, and inclusion.
  • Contribute to SRE / Production support activities.

Required qualifications, capabilities and skills

  • Formal training in software engineering with 4+ years of applied technical experience.

  • Strong hands-on expertise in system design, application development, debugging, fine-tuning and operational support.

  • Deep knowledge of Python (including frameworks like FastAPI)/Java, microservices architecture, and APIs.

  • Proficiency in AWS services such as EC2, ECS, EKS, Lambda, DynamoDB, RDS (Aurora/Postgres), Redshift, EMR, Open Search, Stepfunctions and Kinesis.

  • Experience designing cloud-ready solutions and developing strategies for GenAI and LLM-specific applications.

  • Hands-on experience with Apache Kafka for building and operating event-driven/streaming pipelines.

  • Strong skills in SQL, relational databases, and data modeling (e.g., Aurora RDS, DynamoDB, RDS).

  • Experience with CI/CD tools

  • Ability to solve complex design and functionality challenges independently.

  • Excellent communication and interpersonal skills for collaborating within and across teams.

Preferred qualifications, capabilities and skills

  • Experience working with back-end technologies, containerization (Docker, ECS, EKS), and GenAI agentic frameworks.
  • Hands-on experience with modern cloud technologies, particularly AWS, for scalable AI workflows.
  • Spinnaker experience is preferred

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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

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 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