What are the responsibilities and job description for the Sr Lead Security Engineer - AI/ML Security position at JPMorgan Chase?
Join a team where you can play a crucial role in shaping the future of a world-renowned company and make a direct and meaningful impact in a space designed for top performers.
As a Senior Lead Security Engineer at JPMorgan Chase within the Cybersecurity and Technology Controls organization, you are an integral part of an agile team that works to deliver software solutions that satisfy pre-defined functional and user requirements with the added dimension of preventing misuse, circumvention, and malicious behavior. Drive significant business impact through your capabilities and contributions and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of cybersecurity challenges that span multiple technology domains.
Job responsibilities
- Develop new / update existing AI technology control requirements identified from emerging AI threats, standards and regulations, e.g., MITRE ATLAS, NIST AI Risk Management Framework, EU AI Act, OWASP Top 10 for LLM, etc.
- Engineer / deploy AI specific technology controls in-line with requirements (e.g., model vulnerability management technologies, AI firewalls, etc.) and integrate the controls into the broader JPMC cybersecurity eco-system.
- Partner with other JPMC cybersecurity organizations to uplift their respective areas to accommodate for AI specific security requirements.
- Adds to team culture of diversity, equity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5 years applied experience in AI/ML or cybersecurity or equivalent roles
- Hands-on technologist with extensive experience in Java and Python coding, scripting, and development.
- Subject matter expert in securing AI/ML systems with practical experience in AI and machine learning technologies, including Python, TensorFlow, and PyTorch
- Experienced across the model development lifecycle (MDLC), including data acquisition, model experimentation, training, testing, and MLOps
- Solid understanding of AI system attack surfaces, threats, and mitigating controls throughout the MDLC
- Knowledgeable in AI safety, AI alignment, AI cybersecurity concepts, and trends, including GenAI security
- Strong understanding of cloud computing concepts and services, including AWS and Azure
- Proficient in cloud infrastructure as code (IaC) tools like Terraform
- Knowledgeable in containers and container orchestration technologies such as Docker, Kubernetes, and Helm
- Skilled in planning, designing, and implementing enterprise-level security solutions
- Practical cloud native experience
Preferred qualifications, capabilities, and skills
- AWS Certified Machine Learning – Specialty or Microsoft Certified: Azure Data Scientist Associate
- AWS Certified Security – Specialty or Microsoft Certified: Cybersecurity Architect Expert certification
- CISSP
