
Global financial services firm
Principal Applied AI ML Engineer
必备技能
Python
PyTorch
TensorFlow
Machine Learning
Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what's possible together.
As a Principal AI/ML at JPMorgan Chase within the Corporate Sector – AI/ML & Data Platforms, you will lead a specialized technical area, driving impact across teams, technologies, and projects. In this role, you will leverage your deep knowledge of machine learning, software engineering, and product management to spearhead multiple complex ML projects and initiatives, serving as the primary decision-maker and a catalyst for innovation and solution delivery.
You will be responsible for hiring, leading, and mentoring a team of Machine Learning and Software Engineers, focusing on best practices in ML engineering, with the goal of elevating team performance to produce high-quality, scalable ML solutions with operational excellence. You will engage deeply in technical aspects, reviewing code, mentoring engineers, troubleshooting production ML applications, and enabling new ideas through rapid prototyping. Your passion for parallel distributed computing, big data, cloud engineering, micro-services, automation, and operational excellence will be key.
Job Responsibilities
- Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
- Write production-quality Python code (Py Torch or Tensor Flow as needed) and review critical-path code
- Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
- Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
- Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
- Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
- Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
- Embed responsible AI practices: safety, policy enforcement, audit logging, explainability, and monitoring
- Set engineering standards for agentic AI systems and lead design reviews
- Mentor senior engineers through code reviews and architecture discussions
- Influence roadmap and priorities through technical insight and delivery
Required Qualifications, Capabilities, and Skills:
- 10 years of experience building applied machine learning systems, with recent hands-on work in LLMs or agentic AI
- Strong Python engineering skills; experience with Py Torch or Tensor Flow
- Expertise working with Vector storage systems and designing memory for Agents
- Expertise developing long running agents that run autonomously using tools, skills and human in the loop
- Proven experience deploying LLM-backed services to production (APIs, microservices)
- Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
- Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
- Ability to lead technically and influence outcomes without formal authority
- Experience creating reusable platforms and patterns that accelerate delivery
- Demonstrated commitment to responsible AI practices and operational excellence
- Strong communication and collaboration skills, working across product, risk, legal, and compliance teams
- Experience optimizing inference and ML system performance
Preferred Qualifications, Capabilities, and Skills:
- Experience with fine-tuning, adapters, or custom evaluation frameworks
- Background operating AI systems in regulated environments (finance, healthcare, etc.)
- Experience with prompt engineering and LLM orchestration
- Knowledge of safety filters, audit logging, and explainability in production systems
- Experience mentoring senior engineers and leading architecture discussions
- Demonstrated ability to influence technical roadmaps and priorities
FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.
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关于JPMorgan Chase

JPMorgan Chase
PublicJPMorgan 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
10条评价
3.8
10条评价
工作生活平衡
3.5
薪酬
4.0
企业文化
3.8
职业发展
3.2
管理层
2.8
68%
推荐率
优点
Good benefits and compensation
Supportive colleagues and environment
Flexible work arrangements
缺点
Long hours and heavy workload
Management issues and lack of direction
High stress and expectations
薪资范围
44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
面试评价
4条评价
难度
3.0
/ 5
时长
14-28周
录用率
50%
体验
正面 25%
中性 75%
负面 0%
面试流程
1
Application Review
2
HR Screen
3
Hiring Manager Interview
4
In-person/Final Interview
5
Offer
常见问题
Behavioral/STAR
Past Experience
Culture Fit
Financial Knowledge
Case Study
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