
Global financial services firm
Lead Machine Learning Engineer – Agentic AI, Vice President
必备技能
Python
AWS
Terraform
Machine Learning
Are you passionate about building the next generation of AI solutions? Join us to lead and mentor a team of talented engineers, drive innovation in generative and agentic AI, and deliver impactful, scalable technology for Risk Technology. You’ll collaborate with cross-functional partners and play a key role in shaping the future of Asset and Wealth Management Risk.
As a Lead Machine Learning Engineer – Agentic AI in Risk Technology, you will lead a specialized technical area, driving impact across teams, technologies, and projects. You will leverage your expertise in software engineering and multi-agent system design to deliver complex, high-impact initiatives. You will mentor and guide a team of engineers, foster best practices in ML engineering, and partner with data science, product, and business teams to deliver end-to-end solutions that drive value for the Risk business.
Job responsibilities:
- Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions for the Risk business.
- Design and execute enterprise-wide, reusable AI/ML frameworks and core infrastructure to accelerate AI solution development.
- Develop multi-agent systems for orchestration, agent-to-agent communication, memory, telemetry, and guardrails.
- Guide research on context and prompt engineering techniques to improve prompt-based model performance, utilizing libraries such as SmartSDK and Lang Graph.
- 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 Data Science, 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.
- 10+ years of 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.
- Experience using Lang Graph or SmartSDK for multi-agent orchestration.
- Experience with AWS and infrastructure-as-code tools such as Terraform.
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.
- Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.
- Experience with agentic telemetry and evaluation services.
- Hands-on experience building and maintaining user interfaces.
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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
最新动态
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