Applied AI ML and Context Engineer - Lead
Company: JPMorganChase
Location: Jersey City
Posted on: April 2, 2026
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Job Description:
Description Build what’s next in enterprise AI—solutions that
materially improve how teams make decisions, automate work, and
serve internal customers. In this role, you will take generative AI
from concept to production and help set the standard for semantic
consistency across systems. You will partner closely with
stakeholders to turn complex business needs into measurable
outcomes. You will mentor talent and influence technical direction
across Corporate Technology and supported Corporate Functions. If
you enjoy solving hard problems with real impact, this is the
opportunity. As an Applied AI and Machine Learning Lead in the
Corporate Technology Data Science and AI team , you will design,
build, and deploy scalable analytical and generative AI solutions
that deliver measurable business value. You will collaborate with
stakeholders to shape problem statements, define success metrics,
and deliver production-ready models and intelligent workflows. You
will help establish enterprise semantic modeling standards and a
unified semantic layer that improves trust and consistency across
analytics and AI use cases. We will support you with a
collaborative environment where you can innovate, mentor others,
and continuously grow your skills. Job responsibilities Develop
generative AI, agentic 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. Lead enterprise semantic
modeling strategy, including ontology standards, governance, and
lifecycle management. Create scalable enterprise 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 data pipelines and
frameworks for model training, evaluation, optimization, and
production operations. Implement responsible AI practices, model
risk controls, and governance aligned to regulated environments.
Communicate complex technical concepts clearly to technical and
non-technical stakeholders, including senior leaders, to align
outcomes to business objectives. Mentor engineers and data
scientists and promote modern machine learning engineering best
practices and continuous improvement. Required qualifications,
capabilities, and skills Master’s degree in a data science-related
discipline and 8 years of industry experience, or PhD in a data
science-related discipline. Experience in data analysis,
transformation, and analytics using Python. Demonstrated ability to
develop and maintain production-quality code. Experience with
continuous integration and unit test development. Strong written
and verbal communication skills for technical and business
audiences. Demonstrated scientific thinking and structured
problem-solving skills. Ability to work independently and
collaboratively in a team environment. Experience building and
managing data pipelines and processing workflows. Track record of
delivering actionable insights from data. Demonstrated attention to
detail, curiosity, and ownership in complex analytical work.
Commitment to continuous learning and professional growth in AI and
machine learning. Preferred qualifications, capabilities, and
skills Familiarity with the financial services industry. Experience
with A/B testing and data-driven product development. Knowledge of
cloud-native deployment in large-scale distributed environments.
Experience developing and debugging production-quality machine
learning code. Exposure to prompt engineering practices for large
language models. Exposure to orchestration libraries and frameworks
for large language model applications. Experience implementing
machine learning solutions in business environments. LI-RB1
Keywords: JPMorganChase, Trenton , Applied AI ML and Context Engineer - Lead, IT / Software / Systems , Jersey City, New Jersey
