Job Description

Job Title: Python - AI/ML Security Engineer

Work Location: Hopkins, MN

Job Overview:

As a key member of the AI/ML security Engineer within the broader information security group, this role is responsible for securing generative AI environments and cloud platforms. The primary objective is to ensure the proper implementation of security measures for AI and ML technologies, establish robust processes, and manage daily monitoring and alerting. The ideal candidate will possess a strong understanding of various platforms such as Artificial Intelligence, Machine Learning, and large language models to identify and mitigate risks in these environments.

Key responsibilities:

• Collaborate with architecture, engineering, application, security, and operations teams to address AI and cloud security challenges and drive resolution.
• Define security controls for AI/ML platforms by leveraging a combination of cloud-native and on-premises security tools and applications.
• Conduct security assessments and provide recommendations for IaaS, PaaS, and SaaS cloud environments.
• Develop cloud security requirement documentation for IaaS, PaaS, and CaaS platforms.
• Evaluate and recommend network security and encryption solutions for complex infrastructures.
• Design and implement security controls using Microsoft Defender suite, AWS Security Hub, Google Cloud Security Command Center, and other equivalent security tools.
• Secure containerized environments and identify security vulnerabilities within these systems.
• Work closely with vendors to ensure the proper tools, configurations, and workflows are implemented.
• Develop proposals for data protection using Data Loss Prevention, data discovery or classification, and digital rights management tools.
   

Required Technical Skills:

• 1+ years of experience with AI/ML technologies, including implementing security controls to monitor and protect platforms leveraging these technologies.
• 1+ years of hands-on experience with Google Cloud, Microsoft Azure, and AWS in both Infrastructure as a Service and Platform as a Service environment. Awareness of global data sovereignty and privacy controls, and the ability to translate these into applicable security measures in public cloud environments.
• At least 4 years of experience working with Data Loss Prevention (DLP) tools for website uploads, endpoint data protection, and network-level DLP.
• A minimum of 1 year of experience with security tools for containerized and microservices environments.
• Possession of at least one industry-recognized cloud security certification (e.g., CCSP, CCSK, CCC-PCS).
• A bachelor's degree in technology or a related field, with a minimum of 4 years of experience in various information security domains; or, in the absence of a degree, 7+ years of relevant experience.
   

Preferred Technical Skills:

• Industry-recognized information security certification (e.g., CISSP) is preferred.
• 1 year of experience with Cloud Access Security Broker (CASB) tools such as Bitglass, Netskope, or M-Vision (Skyhigh), specifically for Data Loss Prevention in SaaS-based applications.
• Strong communication skills with the ability to effectively engage stakeholders at varying levels of technical expertise.
• A proven track record of supporting large-scale programs, ideally in a global organization.
• Excellent verbal and written communication abilities, ensuring clarity and precision in conveying complex security concepts.
   

Required Experience:

• Experience with public cloud service providers (e.g. Amazon AWS, Microsoft Azure).
• Strong knowledge of machine learning and security engineering – focused on machine learning (e.g. training data leakage, prompt injection, multi-tenancy workloads, membership inference, etc.).
• Hands-on experience evaluating open-source ML tools, frameworks, and libraries.
• Hands-on experience with commonly used data science programming languages, packages, and tools.
• Hands-on experience with MLOps, DevOps, DataOps, and API integrations.
• Coding skills in - Python, Terraform, and Shell Scripting – Two of these three
• Sec Ops with exposure to AIML. The person needs to know the AI ML lifecycle.
• The basic coding skill on the candidate's preferred technology (guessing Python or Terraform).