DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Lead Machine Learning Engineer Agentic AI within Risk Technology at JPMorgan Chase you will lead a specialized technical area driving impact across teams technologies and this role you will leverage your deep knowledge of software engineering multi-agent system design and leadership to spearhead the delivery of complex and groundbreaking initiatives that will transform Asset and Wealth Management Risk.
You will be responsible for hands-on development and leading and mentoring of 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 systems. You will also engage and partner with data science product and business teams to deliver end-to-end solutions that will drive value for the Risk business.
Responsibilities:
- Lead the deployment and scaling of advanced generative AI Agentic AI and classical ML solutions for the Risk Business.
- Lead design and execution of enterprise-wide reusable AI/ML frameworks and core infrastructure capabilities that will accelerate development of AI solutions.
- Develop multi-agent systems that provide capabilities for orchestration agent-to-agent communication memory telemetry guardrails etc.
- Conduct and guide research on context and prompt engineering techniques to improve the performance of prompt-based models exploring and utilizing Agentic AI libraries like JPMCs SmartSDK and LangGraph.
- Develop and maintain tools and frameworks for prompt-based agent evaluation monitoring and optimization to ensure high reliability at enterprise scale.
- Build and maintain data pipelines and data processing workflows for scalable and efficient consumption of data.
- Develop secure high-quality production code and provide code reviews.
- Foster productive partnership with Data Science Product and Business teams to identify requirements and develop solutions to meet business needs.
- Communicate effectively with both technical and non-technical stakeholders including senior leadership.
- Provide technical leadership mentorship and guidance to junior engineers promoting a culture of excellence continuous learning and professional growth.
Required qualifications capabilities and skills:
- Bachelors degree or Masters in Computer Science Engineering Data Science or related field
- Applied experience in Machine Learning Engineering.
- Strong proficiency in Python and experience deploying end-to-end pipelines on AWS.
- Hands-on practical experience delivering system design application development testing and operational stability
- Hands-on experience using LangGraph or JPMCs SmartSDK for multi-agent orchestration.
- Experience with AWS and Infrastructure-as-code tools like Terraform.
Preferred Qualifications:
- Strategic thinker with the ability to drive technical vision for business impact.
- Demonstrated leadership working effectively 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.
- Demonstrated hands-on experience building and maintaining user interfaces
Required Experience:
Unclear Seniority
DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.As a Lead Machine Learning Engineer Agentic AI within Risk Technology at JPMorgan Chase you will lead a specialized technical area driving impact across teams technologies...
DescriptionWe have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Lead Machine Learning Engineer Agentic AI within Risk Technology at JPMorgan Chase you will lead a specialized technical area driving impact across teams technologies and this role you will leverage your deep knowledge of software engineering multi-agent system design and leadership to spearhead the delivery of complex and groundbreaking initiatives that will transform Asset and Wealth Management Risk.
You will be responsible for hands-on development and leading and mentoring of 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 systems. You will also engage and partner with data science product and business teams to deliver end-to-end solutions that will drive value for the Risk business.
Responsibilities:
- Lead the deployment and scaling of advanced generative AI Agentic AI and classical ML solutions for the Risk Business.
- Lead design and execution of enterprise-wide reusable AI/ML frameworks and core infrastructure capabilities that will accelerate development of AI solutions.
- Develop multi-agent systems that provide capabilities for orchestration agent-to-agent communication memory telemetry guardrails etc.
- Conduct and guide research on context and prompt engineering techniques to improve the performance of prompt-based models exploring and utilizing Agentic AI libraries like JPMCs SmartSDK and LangGraph.
- Develop and maintain tools and frameworks for prompt-based agent evaluation monitoring and optimization to ensure high reliability at enterprise scale.
- Build and maintain data pipelines and data processing workflows for scalable and efficient consumption of data.
- Develop secure high-quality production code and provide code reviews.
- Foster productive partnership with Data Science Product and Business teams to identify requirements and develop solutions to meet business needs.
- Communicate effectively with both technical and non-technical stakeholders including senior leadership.
- Provide technical leadership mentorship and guidance to junior engineers promoting a culture of excellence continuous learning and professional growth.
Required qualifications capabilities and skills:
- Bachelors degree or Masters in Computer Science Engineering Data Science or related field
- Applied experience in Machine Learning Engineering.
- Strong proficiency in Python and experience deploying end-to-end pipelines on AWS.
- Hands-on practical experience delivering system design application development testing and operational stability
- Hands-on experience using LangGraph or JPMCs SmartSDK for multi-agent orchestration.
- Experience with AWS and Infrastructure-as-code tools like Terraform.
Preferred Qualifications:
- Strategic thinker with the ability to drive technical vision for business impact.
- Demonstrated leadership working effectively 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.
- Demonstrated hands-on experience building and maintaining user interfaces
Required Experience:
Unclear Seniority
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