JOB DESCRIPTION

As a Sr. Lead Security Engineer at JPMorgan Chase within the Cybersecurity and Technology Controls line of business, you are an integral part of team that works to deliver Machine Learning solutions that satisfy pre-defined functional and user requirements with the added dimension of detection and prevention of misuse, circumvention, and malicious behavior. As a core technical contributor, you are responsible for carrying out critical technology solutions with tamper-proof, audit defensible methods across multiple technical areas within various business functions.
We are seeking a skilled AI Engineer to join our team, focusing on the integration and deployment of cutting-edge Artificial Intelligence (AI) technologies. The ideal candidate will have a strong foundation in data science and machine learning, with specialized expertise in Large Language Models (LLMs), Vector and Graph Databases. Preferred candidates will also have a working knowledge for how to improve the quality of responses from LLMs in addition to in-depth knowledge of serving private models in an Enterprise setting.

ABOUT US

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.

Required qualifications, capabilities, and skills

• Formal training or certification on security engineering concepts and 5+ years applied experience.
• Strong understanding of Deep Learning and Transformer architectures.
• Proficiency in Deep Learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with RAG frameworks like Langchain or Llamaindex.
• Familiarity with GPU enabled platforms, monitoring tools, and performance optimization strategies.
• Experience with Vector Databases and their relationship with AI models.
• Advanced in one or more programming languages
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• In-depth knowledge of the financial services industry and their IT systems
• Working knowledge of Responsible AI, model fairness, and reliability and safet

Preferred qualifications, capabilities, and skills

• Expertise in cloud platforms (e.g., AWS, GCP, Azure) for AI model deployment.
• Experience integrating or deploying LLM models in production environments.
• Experience with fine-tuning LLMs a plus.
• Experience with Graph databases a plus.
• Experience with developing REST APIs using tools such as Flask or FastAPI.
• Strong communication skills to articulate technical concepts to non-technical audiences.
• Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, or Computer Science, with 3+ years experience working with AI systems and Data Scienc

• Collaborate with domain experts to understand business goals and use cases, leveraging real-world data to solve complex business problems.
• Work with Cybersecurity domain experts to develop and deploy AI models, vector database and Retrieval Augmented Generation (RAG) applications.
• Engineer and maintain infrastructure for private LLM serving, ensuring scalability, reliability, and efficient GPU utilization.
• Implement Vector Databases to enhance information retrieval for Cybersecurity datasets.
• Ensure model interpretability, testability, and compliance with Responsible AI practices.
• Executes creative security solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions and break down technical problems.
• Develops secure and high-quality production code and reviews and debugs code written by others.
• Minimizes security vulnerabilities by following industry insights and governmental regulations to continuously evolve security protocols, including creating processes to determine the effectiveness of current controls.
• Adds to team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on security engineering concepts and 5+ years applied experience.
• Strong understanding of Deep Learning and Transformer architectures.
• Proficiency in Deep Learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with RAG frameworks like Langchain or Llamaindex.
• Familiarity with GPU enabled platforms, monitoring tools, and performance optimization strategies.
• Experience with Vector Databases and their relationship with AI models.
• Advanced in one or more programming languages
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• In-depth knowledge of the financial services industry and their IT systems
• Working knowledge of Responsible AI, model fairness, and reliability and safety

Preferred qualifications, capabilities, and skills

• Expertise in cloud platforms (e.g., AWS, GCP, Azure) for AI model deployment.
• Experience integrating or deploying LLM models in production environments.
• Experience with fine-tuning LLMs a plus.
• Experience with Graph databases a plus.
• Experience with developing REST APIs using tools such as Flask or FastAPI.
• Strong communication skills to articulate technical concepts to non-technical audiences.
• Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, or Computer Science, with 3+ years experience working with AI systems and Data Science