Lead Architect for Agentic AI & Generative Systems

Fairygodboss

Jersey City (NJ)

On-site

USD 180,000 - 260,000

Full time

6 days ago

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Job summary

JPMorganChase’s Applied AI Research team seeks an Applied AI Machine Learning Director to design and deliver agent-based and generative AI solutions transforming complex operations in Commercial & Investment Banking.

You will bridge state-of-the-art research with enterprise engineering, mentor engineers, and collaborate with product, engineering and business teams to drive high-impact outcomes. The role emphasizes safe, scalable, and reliable production systems.

Qualifications

Formal training or certification on applied artificial intelligence and machine learning concepts with 10+ years of applied experience.
Advanced degree (master's or doctorate) in computer science, engineering, statistics, or a related quantitative discipline, or equivalent practical experience.
Demonstrated experience deploying machine learning and/or generative AI systems into production at enterprise scale.
Strong foundation in ML fundamentals, experimental design, and data-driven decision-making.
Experience designing distributed systems for model training, inference, and stateful services in production environments.
Proven ability to create evaluation strategies for agent-based systems and improve them over time.
Strong programming and engineering skills with a track record of building maintainable, reusable components.
Demonstrated ability to lead through influence in cross-functional environments.

Responsibilities

Architect end-to-end agent-based and generative AI solutions to automate complex operational workflows.
Translate ambiguous business problems into research hypotheses, measurable success metrics, and production-ready designs.
Build and ship multiple collaborating agents that coordinate planning and execution across large, multi-step processes.
Design reusable services, libraries, and evaluation frameworks that accelerate adoption across AI and engineering teams.
Establish robust experimentation practices, including offline/online evaluation, monitoring, and iterative improvement loops.
Partner with stakeholders across teams to identify priority use cases, define roadmaps, and deliver scalable capabilities.
Ensure solutions meet enterprise expectations for reliability, security, and long-term maintainability in production.
Mentor and coach engineers and researchers through design reviews and technical guidance.