Senior Application Security Engineer (AI/ML)

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This is a Contract Opportunity with our company that MUST be worked on a W2 Only. No C2C eligibility for this position. Visa Sponsorship is Available! The details are below.

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Position Details

• Contract Length: 6 Months
• Location: Iselin, NJ / Charlotte, NC / Dallas, TX / Phoenix, AZ
• Schedule: 3 days onsite / 2 days remote (no fully remote option)
• Ref#: 246279

This role focuses on integrating Static Application Security Testing (SAST) tools with AI/ML-driven remediation capabilities. The engineer will enhance security pipelines by leveraging AI and machine learning to improve shift-left security practices and automate code remediation within application workflows.

Key Responsibilities

• Develop and implement SAST strategies for AI/ML-based code
• Customize and integrate SAST tools with LLMs and AI/ML frameworks
• Automate testing and integrate security tools into CI/CD pipelines
• Troubleshoot and resolve tool and integration issues
• Provide hands-on technical support for Checkmarx and Checkmarx ONE platforms
• Drive vendor accountability to address product defects
• Support patching and upgrade testing
• Collaborate with Product Owners, Application Security Champions, developers, and offshore partners
• Maintain accurate and up-to-date documentation reflecting process changes

Required Skills & Experience (Must Have)

• 4 years of application security experience
• 3 years of AI/ML development experience (e.g., TensorFlow, PyTorch)
• 2 years working with generative AI models, reinforcement learning, or auto-code remediation models
• 2 years of Agile experience using tools such as Jira and Confluence (or equivalent)
• Strong understanding of OWASP Top 10
• Strong verbal, written, and interpersonal communication skills
• Strong customer service skills
• Ability to troubleshoot complex technology integration issues

Desired Skills

• 2 years of cloud experience
• 3 years of scripting experience (Python)
• 2 years of SQL experience
• 1 year of experience with Corgea AI

Additional Requirements

• Must be able to work onsite in a hybrid model (3 days per week)
• Must be able to collaborate across distributed teams and stakeholders