JPMorgan Lead Security Engineer – AI / ML Laboratory Technical Application Owner 

United States, Ohio, Columbus 

209172660

Job responsibilities

• Facilitates security requirements clarification for multiple networks to enable multi-level security to satisfy organizational needs
• Works with stakeholders and senior business leaders to recommend business modifications during periods of vulnerability
• Be responsible for triaging based on risk assessments of various threats and managing resources to cover impact of disruptive events
• Adds to team culture of diversity, opportunity, inclusion, and respect
• Oversees the integration and functionality of the platform with AWS services. Ensuring the platform supports the full ML lifecycle, including data preparation, model training, testing, and deployment.
• Ensuries that the platform adheres to JP Morgan Chase's data protection policies and AWS security best practices. Implementing access controls and encryption to safeguard sensitive data and models.
• Monitors the performance of ML models and the platform's infrastructure on AWS. Optimizing resource usage and scaling capabilities to handle varying workloads efficiently.
• Collaborates with data scientists, developers, and business units to understand requirements and provide updates on platform capabilities. Facilitating training sessions and support for users of the platform.
• Identifies risks related to model accuracy, data privacy, and cloud service disruptions. Implementing strategies to mitigate risks, such as regular model evaluations and backup solutions.
• Guides new users through sandbox setup and access; Access Management: Manage secure user permissions and access; Experiment Support: Assist with running and troubleshooting experiments; Results Analysis: Help close experiments and analyze outcomes; Decision Facilitation: Lead result reviews and support go/no-go decisions and readouts with leadership; Continuous Engagement: Maintain user communication for feedback and improvements

Required qualifications, capabilities, and skills

• Formal training or certification on Security Engineering concepts and 3+ years applied experience
• Skilled in planning, designing, and implementing enterprise-level security solutions
• Advanced in one or more programming languages
• Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Extensive experience with threat modeling, discovery, vulnerability, and penetration testing
• Ability to tackle design and functionality problems independently with little to no oversight
• Proficiency in AWS services, including EC2, S3, Lambda, SageMaker, and other relevant tools for ML model deployment. Strong understanding of machine learning concepts and workflows. Strong experience in terraform.
• Ability to write and maintain scripts for automation and patch deployment. Familiarity with programming languages such as Python, Java, or others used in ML and cloud environments.
• Experience in managing cloud-based infrastructure and applications. Knowledge of system monitoring tools and techniques.
• Understanding of cybersecurity principles and practices, especially in cloud environments. Ability to implement security patches and ensure compliance with industry standards.
• Ability to diagnose and resolve technical issues related to the platform and its integration with AWS. Strong analytical skills to assess the impact of patches and updates.
• Skills in managing projects related to patch creation and deployment. Ability to coordinate with cross-functional teams and manage timelines effectively.