Applied AI ML Director - AI Agents and Agentic Systems

You will join a platform team shaping how agentic systems are built, evaluated, and deployed s is a deeply hands-on role where you will design core platform capabilities, write production-quality code, and partner across product and engineering to drive adoption. You will help turn advanced agent and generative AI techniques into reliable, reusable building blocks that teams can use to deliver measurable business outcomes.

You will play a critical role in evaluating state of the art in agentic systems by leveraging thorough scientific experimentation and evaluations of GenAI and agentic capabilities.

As an Applied Artificial Intelligence and Machine Learning Director at JPMorgan

Chase within the Agent Builder Platform team in Corporate Sector, you will build and evolve an Agent software development kit (SDK) and specialized agent capabilities for broad enterprise use. You will own key technical decisions, set practical engineering standards, and deliver critical components end-to-end. You will collaborate with partners across data, platform, and application teams to ensure solutions are scalable, secure, and maintainable.

Job responsibilities

Architect and implement core Agent SDK capabilities and reference implementations, with a strong emphasis on production-ready code Build specialized agents and reusable agent components, improving reliability, observability, and evaluation quality over time Translate emerging agentic and generative AI techniques into scalable platform features that teams can adopt with minimal friction

Design and implement evaluation approaches for agent behavior, including quality, robustness, latency, and cost trade-offs Develop and optimize model-serving and workflow patterns for agentic systems, including agentic orchestration, harness, tool use, and other advanced constructs.

Partner with product and engineering stakeholders to align platform capabilities to clear success metrics and prioritized outcomes

Drive technical decisions by clarifying ambiguity, identifying trade-offs, and producing crisp recommendations and designs

Improve the agentic platform’s performance, accuracy, and efficiency through profiling, bottleneck analysis, and system-level optimization

Required qualifications, capabilities and skills

Formal training or certification on applied artificial intelligence and machine learning concepts and 10+ years applied experience
10+ years of hands-on experience building large-scale machine learning systems and platform services used by multiple teams

Strong software engineering skills as applied to real-life ML/AI areas, including the ability to own end-to-end delivery from design through implementation, testing, and operation

Extensive experience with machine learning frameworks such as PyTorch or Tensor Flow Hands -on experience with agentic and generative AI system design, including tool use, planning patterns, retrieval-augmented generation, and evaluation methods

Strong experience with cloud and Kubernetes ecosystems, including building and operating production workloads

Background in high-performance machine learning systems, including hardware acceleration considerations (for example, GPU optimization)
Proven ability to influence across teams without formal authority through technical leadership, clear communication, and strong execution

Preferred qualifications, capabilities and skills

Experience contributing to or optimizing open-source machine learning frameworks or platform tooling

Experience building ML systems for production-grade AI workloads, including GenAI and agentic solutions.

Experience with different open source ML/AI and agentic frameworks, LLM training frameworks, or additional machine learning ecosystem tools beyond primary frameworks

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