Job description

You will join a platform team shaping how agentic systems are built, evaluated, and deployed at scale. This 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 JPMorganChase 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 TensorFlow • 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 • Advanced degree in Computer Science, Machine Learning, or a related field • Experience establishing applied science and engineering standards for responsible and reliable AI systems, including testing and measurement practices, rigorous evaluation and benchmarking methods. FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries. #LI-RB3 #AMDPAIML Hands-on applied science leader building, researching, evaluating, and productionizing agentic capabilities in Agent Builder.

Requirements

Must-haves

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 TensorFlow
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
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
Advanced degree in Computer Science, Machine Learning, or a related field
Experience establishing applied science and engineering standards for responsible and reliable AI systems, including testing and measurement practices, rigorous evaluation and benchmarking methods.
Hands-on applied science leader building, researching, evaluating, and productionizing agentic capabilities in Agent Builder.

Nice-to-haves

Preferred qualifications, capabilities and skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlkubernetesllm

Platform: oracle-cloud
Effort: medium