Global Financial Crimes Tech - Applied AI ML Director

JPMorgan Chase & Co.; Greater London; On-site; GBP 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in London seeks an Executive Director to lead ML engineering for global financial crimes monitoring. You will design and productionize risk models and LLM-based explainability at scale across high-volume banking transactions.

You will manage a team of ML engineers, collaborate with product, SRE and compliance SMEs, and drive data pipelines in Databricks, while advancing governance, auditability and cloud-scale deployment.

Qualifications

• PhD/MS/BS in Computer Science, Data Science, AI or related field.

Responsibilities

• Lead ML engineers to deliver AI solutions in production.
• Design, build, and operate ML models.
• Design, build and operate LLM solutions.
• Design feedback/accuracy measurement techniques for AI solutions.
• Design data pipelines for risk features in Databricks.
• Monitor drift, bias and performance degradation.
• Collaborate with Product Managers, SRE and Compliance SMEs to improve outcomes.

Skills

ML leadership

NLP expertise

Leadership

Production ML

Communication

Education

PhD/MS/BS in CS/DS/AI

Tools

Databricks

AWS

PyTorch

TensorFlow

HuggingFace Transformers

Java

Python

Job description

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