Senior AI/ML Security Engineer

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., * 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.

• 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.
• Ability to assess and prioritize risks involving AI models, prompts, datasets, agents, and AI-enabled applications.
• Strong understanding of AI/ML attack vectors, including:
• Prompt injection
• Data and model poisoning
• Model extraction and inversion
• Adversarial inputs and examples
• AI/ML supply-chain vulnerabilities
• Excessive agent permissions and unsafe tool usage
• Sensitive-data exposure
• Familiarity with industry guidance such as the OWASP security frameworks for LLM and machine-learning applications.
• Experience with model vulnerability scanning, model security assessment, or similar AI security tooling.
• Understanding of common ML model and serialization formats such as Pickle, TensorFlow formats, and SafeTensors.
• Familiarity with both structured and unstructured data environments, including SQL databases, data warehouses, object storage, and NoSQL platforms.
• Understanding of cloud, container, microservices, and application security principles.
• Excellent analytical and problem-solving skills.

Technical Environment

Experience with several of the following technologies would be valuable:

AI/ML & Data

• MLflow
• Kubeflow
• SageMaker
• Databricks
• RAG architectures
• LLM and agent frameworks

Development

• Python
• Java
• C# / .NET
• JavaScript
• Go
• REST APIs
• Microservices

DevSecOps / CI/CD

• Git
• Bitbucket
• Jenkins
• Artifactory
• Nexus
• SonarQube
• Snyk
• Jira
• Automated security scanning

Preferred Background

• Previous experience as a software engineer or software architect before moving into security.
• Experience implementing model-scanning capabilities across an enterprise ML development environment.
• Experience defining security architecture for GenAI or agentic AI platforms.
• Familiarity with AI Security Posture Management concepts and tooling.
• Strong knowledge of secure design patterns for cloud-native and containerized applications.
• Experience working within Agile engineering organizations.
• Experience communicating emerging technical risks to engineering leadership and non-security stakeholders.

Education & Certifications

• Bachelor’s degree in Computer Science, Cybersecurity, Computer Engineering, Information Systems, or a related technical discipline, or equivalent professional experience.
• Security certifications such as CISSP, CSSLP, CEH, GCIA, GPEN, or GWAPT are beneficial but not required.