Applied AI ML Director - AGENT BUILDER PLATFORM

JPMorganChase•Palo Alto, CA

About The Position

Shape the future of AI at scale by leading the team behind the firm’s core Agent SDK. As Director of ML Engineering, you will drive the technical vision and execution for the foundational toolkit that enables long-running, autonomous AI agents. You’ll collaborate with top talent in data science, engineering, and product to turn cutting-edge research into resilient, production-ready systems. This is your opportunity to make a lasting impact on enterprise AI and agentic system design. If you are passionate about building and scaling high-performing teams and developer platforms, we want to connect with you. Job Summary As a Director of Applied AI ML Engineering in the Agent Builder Platform team within the Corporate AI ML Technology Team, you will own the technical vision and delivery of the Agent SDK and the agentic systems it enables. You will lead a multidisciplinary team to translate research into robust, observable, and responsible agent solutions. Together, we drive innovation in agentic workflows, data science rigor, and safe AI practices. You will have the opportunity to shape the firm’s approach to autonomous agents and empower teams across the organization.

Requirements

10 years of experience in machine learning engineering, applied data science, or ML platform development.
3 years of experience in a leadership role managing teams of engineers and/or data scientists.
Strong technical depth across the ML and data science stack, including ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn) and LLM serving and fine-tuning toolchains.
Proven experience designing and delivering SDKs, platforms, or agent development kit, including API design and documentation strategy.
Expertise in distributed and long-running systems, including state machines, workflow orchestration, checkpointing, and fault-tolerant design.
Fluency in LLM-based agent architectures, prompt engineering, tool use, and multi-agent coordination patterns.
Demonstrated ability to craft and drive a technical vision that maximizes business impact and influences decision-making.
Proven ability to build, mentor, and retain senior technical talent and foster a collaborative team culture.
Strong foundation in experimental design, statistical analysis, and evaluation methodology.
Excellent communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
Experience integrating data science rigor and responsible AI practices into production systems.

Nice To Haves

Experience building or contributing to open-source ML or agent frameworks such as LangChain, AutoGen, Haystack, or MLflow.
Background in ML evaluation and monitoring at scale, including drift detection, A/B testing, and automated regression testing.
Deep familiarity with multi-agent system design, including communication protocols, task delegation, and conflict resolution.
Experience overseeing AI workload deployment on managed ML platforms such as AWS SageMaker or Bedrock.
Background leading AI engineering in regulated or high-reliability environments, especially financial services or asset and wealth management.
Experience integrating user and stakeholder feedback loops into continuous model and system improvement processes.
Experience designing developer experience, writing technical documentation, and supporting internal developer adoption.

Responsibilities

Define and drive the technical vision and roadmap for the Agent SDK and long-running agentic workflows.
Set architectural direction for SDK components, including task orchestration, state management, checkpointing, and retry logic.
Champion data science rigor by establishing measurement, experimentation, and evaluation frameworks for agent performance.
Oversee the design and optimization of ML pipelines for training, fine-tuning, and inference of models powering agent intelligence.
Direct the instrumentation strategy for observability, feedback loops, and continuous improvement of autonomous agents.
Guide the adoption and extension of agent frameworks, supporting multi-step reasoning, tool use, and multi-agent coordination.
Build, mentor, and scale a high-performing team of ML engineers, data scientists, and platform engineers.
Collaborate with engineering, data science, product, and business stakeholders to align the team’s roadmap with enterprise AI strategy.
Serve as the primary technical point of contact for the Agent SDK platform, communicating complex trade-offs to diverse audiences.
Champion safe, responsible, and compliant agent systems by implementing guardrails and policy enforcement mechanisms.
Foster a collaborative environment where research insight translates into production impact.