Job description

You will help shape how analytics and generative AI are delivered at enterprise scale—turning complex business problems into production-ready, measurable solutions. You will work with a collaborative team that values engineering excellence, responsible innovation, and continuous learning. As an Applied AI and Machine Learning Lead at JPMorganChase within Corporate Technology Data Science and AI , you will design, build, and deploy scalable analytical and generative AI solutions that deliver measurable business value. You will partner with stakeholders to shape problem statements, define success metrics, and deliver production-ready models and intelligent workflows. You will help establish semantic modeling standards and a unified semantic layer that improves trust and consistency across analytics and AI use cases. Job responsibilities • Develop generative AI, agent-based AI, and large language model solutions in Python from proof of concept through production deployment • Design context engineering approaches to improve model accuracy, latency, reliability, and overall performance in real-world workflows • Lead semantic modeling strategy, including ontology standards, governance, and lifecycle management aligned to enterprise needs • Create scalable ontologies that model business entities, relationships, rules, and constraints in partnership with domain experts • Define semantic integration patterns across data pipelines, application programming interfaces, data contracts, and experience layers to resolve semantic conflicts • Build and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems • Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration methods • Build and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and production operations • Implement responsible AI practices, model risk controls, and governance aligned to regulated environments and internal standards • Communicate complex technical concepts clearly to technical and non-technical stakeholders, including senior leaders, to align delivery to business objectives Required qualifications, capabilities and skills • Master’s degree in a data science-related discipline and 8 years of industry experience, or a PhD in a data science-related discipline • Hands-on experience developing and deploying machine learning and generative AI solutions using Python • Demonstrated ability to write and maintain production-quality code, including reliability, performance, and maintainability considerations • Experience with continuous integration practices and unit test development to support quality delivery • Experience building and managing data pipelines and processing workflows that support analytical and machine learning use cases • Strong written and verbal communication skills, including the ability to translate technical decisions into business impact • Demonstrated scientific thinking and structured problem-solving skills for ambiguous, data-driven challenges • Ability to work independently while collaborating effectively across product, engineering, and business stakeholders Preferred qualifications, capabilities and skills • Experience building large language model applications that use context engineering to improve response quality and reliability • Background in semantic modeling, ontology design, and governance practices in enterprise environments • Experience designing semantic integration patterns across data contracts and application programming interfaces in distributed systems • Experience implementing monitoring and evaluation approaches for machine learning and generative AI in production • Experience mentoring data scientists and engineers and promoting modern machine learning engineering best practices #LI-RB3 Design and deliver scalable generative AI and semantic solutions that improve decisioning, automation, and trust in data.

Requirements

Must-haves

Develop generative AI, agent-based AI, and large language model solutions in Python from proof of concept through production deployment
Design context engineering approaches to improve model accuracy, latency, reliability, and overall performance in real-world workflows
Lead semantic modeling strategy, including ontology standards, governance, and lifecycle management aligned to enterprise needs
Create scalable ontologies that model business entities, relationships, rules, and constraints in partnership with domain experts
Define semantic integration patterns across data pipelines, application programming interfaces, data contracts, and experience layers to resolve semantic conflicts
Build and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems
Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration methods
Build and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and production operations
Implement responsible AI practices, model risk controls, and governance aligned to regulated environments and internal standards
Communicate complex technical concepts clearly to technical and non-technical stakeholders, including senior leaders, to align delivery to business objectives
Required qualifications, capabilities and skills
Master’s degree in a data science-related discipline and 8 years of industry experience, or a PhD in a data science-related discipline
Hands-on experience developing and deploying machine learning and generative AI solutions using Python
Demonstrated ability to write and maintain production-quality code, including reliability, performance, and maintainability considerations
Experience with continuous integration practices and unit test development to support quality delivery
Experience building and managing data pipelines and processing workflows that support analytical and machine learning use cases
Strong written and verbal communication skills, including the ability to translate technical decisions into business impact
Demonstrated scientific thinking and structured problem-solving skills for ambiguous, data-driven challenges
Ability to work independently while collaborating effectively across product, engineering, and business stakeholders
Experience building large language model applications that use context engineering to improve response quality and reliability
Background in semantic modeling, ontology design, and governance practices in enterprise environments
Experience designing semantic integration patterns across data contracts and application programming interfaces in distributed systems
Experience implementing monitoring and evaluation approaches for machine learning and generative AI in production

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 learningpython

Platform: oracle-cloud
Effort: medium