
Lead Security Engineer – AI / ML Laboratory Technical Application Owner
- Columbus, OH
- Permanent
- Full-time
- 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
- 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.