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职位JPMorgan Chase

Software Engineer III - Python AI/ML

JPMorgan Chase

Software Engineer III - Python AI/ML

JPMorgan Chase

Jersey City, NJ, United States, US

·

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 a Software Engineer III at JPMorgan Chase within Corporate Technology you will be contributing to the firm's document Management Platform group offering platform as a Service for all things documents. From storage, to digitization, Search and User workflows. We cater to multiple LOBs across the firm. The Patented solutions of this group is built of varying tech stacks from Java Spring-boot, Micro-front end approach, Micro services deployed on AWS , In House ML Models for Digitization, Gen AI for Document extraction & et al.

Job Responsibilities:

  • Build and maintain Spring Boot microservices, RESTful APIs, and event-driven integrations (e.g., Kafka).
  • Implement robust API contracts, input validation, error handling, and observability (logs, metrics, traces).
  • Write high-quality code with comprehensive unit, integration, and end-to-end tests; automate with CI/CD (e.g., Jenkins/GitHub Actions).
  • Optimize performance, reliability, and cost; tune SQL/NoSQL queries and caching.
  • Develop responsive web UIs with React (TypeScript) or Angular, integrating securely with backend APIs.
  • Enforce accessibility, performance budgets, and component reusability; implement e2e tests (e.g., Cypress/Playwright).
  • Build cloud-ready services on AWS (e.g., ECS/EKS, Lambda, S3, RDS/DynamoDB, IAM, CloudWatch) and manage IaC (Terraform/CloudFormation).
  • Implement secure secrets management, least-privilege IAM, encryption in transit/at rest, and runtime SLOs.
  • Apply secure coding practices and threat modeling; remediate vulnerabilities promptly.
  • Conduct design and code reviews; mentor peers and contribute to engineering standards and documentation.
  • Partner with data scientists to integrate approved AI/ML models into services (batch or real-time) via stable APIs, with versioning, feature/data pipelines, and runtime monitoring using firm-vetted tools.

Required qualifications, capabilities and skills

  • Strong proficiency in Java, Spring Boot, and API/microservice design.
  • Front-end experience with React + TypeScript or Angular.
  • Proficiency with SQL and at least one NoSQL store; messaging/streaming (Kafka/Kinesis).
  • CI/CD pipelines, Docker, and experience deploying to AWS.
  • Solid understanding of security fundamentals, identity/authorization, and compliance-aware design.
  • Clear communication and collaboration skills in multi-team environments.

Preferred qualifications

  • AWS, performance tuning for low-latency services, and advanced observability.
  • Experience with feature flags, blue/green or canary deployments, and A/B testing.
  • Familiarity with model serving patterns, feature stores, or ML governance in enterprise settings.
  • Bachelor’s/Master’s in Computer Science or related field (or equivalent practical experience).

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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