Lead Machine Learning Engineer – Agentic AI, Vice President

JPMorgan Chase•Jersey City, NJ

About The Position

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.

Requirements

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 LangGraph or SmartSDK for multi-agent orchestration.
Experience with AWS and infrastructure-as-code tools such as Terraform.

Nice To Haves

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.

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 LangGraph.
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.