Lead Software Engineer - AI

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.