We have an exciting and rewarding opportunity for you to take your ML engineering leadership career to the next level. We are building a next generation, AI-driven Global Financial Crimes Strategic Monitoring solutions that detects AML risk, regulatory violations, transactions risk, misconduct, and behavioral anomalies.
As an AI ML Director at JPMorganChase within the Corporate Technology, Compliance, Conduct & Operational Risk (CCOR) organisation, you will lead the team and by example through design, build and productionize Risk typologies/ features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability that operate at scale across high-volume banking transactions. You will work at the intersection of Risk modeling, and NLP architectures, inference systems, regulatory explainability and auditability. This is a hands-on, Executive Director level role requiring deep expertise in ML operations, LLM integration, scalable ML systems and production grade engineering discipline. This role offers a chance for you to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies.
Job responsibilities
Lead a group of talented ML engineers and deliver AI solutions in production
Design, build, collaborate, and operate ML models
Design, build and operate LLM solutions
Design and build feedback and accuracy measurement techniques for AI solutions
Design, build, and operate risk features data pipelines in Databricks
Conduct monitoring to detect and alert drift, bias and performance degradation
Work closely within a cross-functional team following agile based processes
Collaborate closely with Product Managers, SRE and Compliance SMEs to continuously improve product adoption, reliability and outcomes
Required qualifications, capabilities, and skills
Formal training or certification in Computer Science, Data Science, AI concepts and advanced applied experience
PhD, MS or BS in Computer Science, Data Science, AI or similar fields with of experience
Deep foundation in Information Retrieval, Natural Language Processing and
Expert in functional programming and JVM based languages- Python, Java
Experience integrating models into cloud scale, microservices based architectures
Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
Hands-on experience with AWS services, and Databricks
Experience/Exposure to SQL, NoSQL and messaging stacks
Excellent verbal & written communication skills and bias for action and ownership in early stage env
Operational experience in supporting an enterprise grade ML application in production
Preferred qualifications, capabilities, and skills
Knowledge of Databricks CDAO platform is good to have
Experience with building production-grade ML pipelines, APIs and MLOps frameworks
Experience in AML, monitoring and investigations systems is a strong plus
Good understanding of data engineering concepts, distributed systems, and scalable architectures
Familiarity with vector databases, model serving, and inference optimization is a plus
About the company
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About the team
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.
Summary
Lead ML engineers to design, build, and operate production AI solutions for financial crimes monitoring, including risk typologies, LLM solutions, and Databricks data pipelines. Requires a CS/Data Science/AI degree, deep NLP and information retrieval expertise, Python/Java, cloud microservices experience, and ML frameworks such as PyTorch or TensorFlow.
Responsibilities
Lead ML engineers and deliver AI solutions in production; design, build, and operate ML models; design, build, and operate LLM solutions; build feedback and accuracy measurement techniques; build risk feature data pipelines in Databricks; monitor drift, bias, and performance degradation; work in cross-functional agile teams; collaborate with Product Managers, SRE, and Compliance SMEs
Qualifications
PhD, MS or BS in Computer Science, Data Science, AI or similar fields; deep foundation in Information Retrieval and NLP; expert in functional programming and JVM languages Python/Java; experience integrating models into cloud microservices; AWS/Databricks; SQL/NoSQL/messaging; enterprise ML production experience
Education requirements
PhD, MS or BS in Computer Science, Data Science, AI or similar fields
Experience requirements
Deep foundation in Information Retrieval and NLP; expert in functional programming and JVM languages Python/Java; experience integrating models into cloud scale microservices; experience with ML frameworks; hands-on AWS and Databricks; SQL/NoSQL/messaging exposure; operational experience supporting enterprise ML applications in production