Job description

As an Applied AI and Machine Learning Lead at JPMorganChase within the Data Science and AI team supporting our Technology organization , you will drive high-impact generative AI and advanced analytics initiatives from research through production. You will lead the design, evaluation, and deployment of foundation-model and agentic AI capabilities that improve decision-making, automate work, and raise engineering standards for reliability and governance. You will partner with product, engineering, and risk stakeholders to define measurable outcomes and deliver solutions that are scalable, robust, and responsibly deployed. Job Responsibilities • Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption. • Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls. • Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps. • Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls. • Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts. • Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes. • Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations. • Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity. • Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism. Required Qualifications, Capabilities, and Skills • Master’s degree in computer science, engineering, statistics, or a related quantitative discipline. • 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments. • Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch). • Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders. • Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs. • Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments. • Strong software engineering fundamentals, including version control, code review, testing, and maintainable design. • Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans. Preferred Qualifications, Capabilities, and Skills • PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI. • Publications, patents, or open-source contributions that demonstrate research depth and practical impact. • Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows. • Experience building agentic AI systems (tool use, orchestration, planning, and workflow automation) with strong evaluation discipline. • Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases. • Experience applying AI solutions within financial services or other highly regulated industries. Lead foundation-model and agentic AI delivery to bring production-ready generative AI and trusted analytics to enterprise teams.

Requirements

Must-haves

Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism.
Required Qualifications, Capabilities, and Skills
Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch).
Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments.
Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.
PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI.
Publications, patents, or open-source contributions that demonstrate research depth and practical impact.
Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows.
Experience building agentic AI systems (tool use, orchestration, planning, and workflow automation) with strong evaluation discipline.
Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases.

Nice-to-haves

Preferred Qualifications, Capabilities, and Skills

Application prep

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Keywords to mirror

aimachine learningpython

Platform: oracle-cloud
Effort: medium