As a Lead Software Engineer for AI – Vice President at JPMorgan Chase 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, multi-agent system design, data science, and NLQ to deliver complex, high-impact initiatives. You will mentor and guide a team of engineers, foster best practices in AI 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 and agentic AI solutions for the Risk business, with a focus on natural language querying of structured and unstructured data sources.
* Design and execute enterprise-wide, reusable AI frameworks and core infrastructure to accelerate AI solution development, including NLQ capabilities for diverse data types.
* Develop multi-agent systems for orchestration, agent-to-agent communication, memory, telemetry, guardrails, and NLQ-driven data retrieval and processing.
* Guide research on context and prompt engineering techniques to improve prompt-based model performance and NLQ accuracy, utilizing libraries such as LangGraph.
* Develop and maintain tools and frameworks for prompt-based agent evaluation, monitoring, and optimization at enterprise scale, with emphasis on NLQ workflows and orchestration.
* Build and maintain data pipelines and processing workflows for scalable, efficient consumption and querying of structured and unstructured data via natural language interfaces.
* Write secure, high-quality production code and conduct code reviews.
* Partner with Data Science, Product, and Business teams to identify requirements and develop NLQ-enabled 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.
* Experience in data science and natural language querying, including experience deploying end-to-end pipelines on AWS.
* Strong proficiency in Python.
* Hands-on experience in system design, application development, testing, and operational stability.
* Experience using LangGraph for multi-agent orchestration and NLQ integration.
* 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.
* Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows.
* Demonstrated leadership working with engineers, data scientists, and AI practitioners.
* Familiarity with MLOps practices and AI pipelines.
* Hands-on experience building and maintaining user interfaces for NLQ and data exploration.
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