Senior AI/ML Security Engineer

Position Overview

We are seeking a senior-level AI/ML Security Engineer to help establish and mature security practices across machine learning, generative AI, and agentic development environments.

This is a Hybrid onsite engagement, located in NJ 3 days a week, ideally paying on a W2 hourly basis.

This role sits at the intersection of cybersecurity, software engineering, MLOps, and emerging AI technologies. The engineer will be responsible for identifying risks within AI/ML systems and building practical security controls into the platforms, pipelines, and development processes used to create and deploy them.

A major focus will be securing the rapidly evolving use of LLMs, AI coding assistants, autonomous agents, RAG architectures, and agentic software-development workflows. The ideal candidate is highly technical and capable of both defining security strategy and implementing controls through code and automation.

Key Responsibilities

• Design and implement security controls throughout AI/ML and MLOps pipelines, including data ingestion, model development, validation, storage, deployment, and inference.
• Assess security risks associated with machine learning models, LLM applications, AI agents, datasets, prompts, and AI-enabled applications.
• Evaluate the security of AI coding assistants, coding agents, autonomous agents, and agentic workflows used within software engineering organizations.
• Establish security guardrails for AI-assisted development platforms, agent orchestration frameworks, and autonomous development pipelines.
• Help develop an enterprise framework for securing the Agentic Development Lifecycle (ADLC), incorporating threat modeling, secure development requirements, testing, deployment controls, approvals, and continuous monitoring.
• Evaluate AI Security Posture Management (AI-SPM) capabilities and establish processes for discovering, classifying, prioritizing, and remediating AI-related security risks.
• Assess emerging attacks against LLMs, frontier AI models, and autonomous agents and translate those risks into preventative and detective security controls.
• Develop and maintain automation and security capabilities using Python and CI/CD technologies.
• Integrate model and AI security scanning into development pipelines as part of a shift-left security strategy.
• Analyze model-scanning and vulnerability-assessment results and partner with engineering teams on remediation.
• Assess model inference and deployment architectures with consideration for security, performance, scalability, and resource utilization.
• Evaluate security surrounding agent sandboxes, runtime environments, tool access, permissions, agent-to-tool communication, and execution workflows.
• Establish controls governing autonomous agent behavior, including permissions, approval mechanisms, runtime restrictions, secrets management, and data-access boundaries.
• Partner closely with application security, platform engineering, software development, data science, and machine learning teams.
• Research emerging AI security threats and recommend improvements to enterprise security architecture and engineering practices.

Required Experience

• 8+ years of experience across software engineering, cybersecurity, application security, platform engineering, or related technical disciplines.
• 5+ years of hands-on software engineering or development experience.
• Strong understanding of AI/ML security, GenAI security, and agentic AI risks.
• Hands-on experience building or supporting MLOps pipelines and model deployment environments.
• Experience with platforms such as MLflow, Kubeflow, AWS SageMaker, or comparable MLOps technologies.
• Strong Python programming and automation skills.
• Strong understanding of modern CI/CD pipelines and secure software-development practices.
• Experience incorporating security testing or scanning into automated development pipelines.
• Hands-on familiarity with AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, Windsurf, Microsoft Copilot, or similar platforms.
• Experience using AI-assisted engineering techniques across one or more languages such as Python, Java, JavaScript, C#, .NET, or Go.
• Strong understanding of LLMs, AI agents, autonomous workflows, RAG architectures, tool-calling systems, and agent orchestration.
• Experience assessing the security implications of agent runtime environments, sandboxing, tool permissions, and autonomous execution.
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