Sr. Machine Learning Security Operations (MLSecOps) Engineer

Location: Roseland, New Jersey About The Role We are seeking a highly skilled and motivated Sr. Machine Learning Security Operations (MLSecOps) Engineer to join our team. This role will be pivotal in ensuring the security, integrity, and compliance of our machine learning (ML) and artificial intelligence (AI) systems throughout their lifecycle—from data ingestion and model development to deployment and inference. You will collaborate with ML engineers, data scientists, and security teams to design and implement secure ML pipelines, proactively identify vulnerabilities, and safeguard AI/ML assets against evolving threats. This position requires a blend of expertise in cybersecurity, MLOps, software engineering, and modern cloud-native environments. Key Responsibilities • Design, implement, and maintain secure ML pipelines for model evaluation, validation, deployment, and inference. • Assess and mitigate security risks across the ML lifecycle, including data ingestion, model storage, deployment, and inference environments. • Develop and maintain automation code for ML pipelines using Python and CI/CD tools, ensuring compliance with best practices and robust security controls. • Institutionalize “shift-left” security scanning of AI/ML models; interpret results and remediate vulnerabilities. • Monitor and secure structured and unstructured data storage systems used in ML workflows. • Stay updated on vulnerabilities affecting ML models, LLMs, and AI agents (e.g., prompt injection, data poisoning, model theft, adversarial attacks). • Evaluate and optimize model deployment strategies for performance, security, and scalability. • Build and roll out solutions for generating AI/ML Bill of Materials (AI-BoM). • Drive adoption of secure ML practices across engineering, product, and security teams. • Document and communicate security practices, risks, and controls to both technical and non-technical stakeholders. • Perform additional duties as needed in a fast-paced, security-first AI environment. • Education • Bachelor’s degree in Computer Science, Information Security, Computer Engineering, or related field, or equivalent professional experience. • Preferred Qualifications (Nice to Have) • Experience developing RESTful web services and working in Java, C#, .NET, or JavaScript. • Familiarity with AI supply chain risks and secure deployment of GenAI/LLM applications. • Professional certifications such as CEH, CISSP, CSSLP, GCIA, GPEN, or GWAPT. • Required Qualifications & Skills • 8+ years of overall IT or cybersecurity experience, with 5+ years in software engineering. • Proven hands-on experience with MLOps pipelines and deployment tools (Kubeflow, MLflow, SageMaker, etc.). • Strong programming skills in Python and CI/CD automation (Bitbucket, Jenkins, Git, Artifactory, Nexus, SonarQube, Snyk, etc.). • Deep understanding of ML model training and inference algorithms, with knowledge of their security implications. • Familiarity with structured data systems (SQL, data warehouses) and unstructured systems (NoSQL, object storage). • Experience with Databricks and ML security tools for model scanning/vulnerability assessment. • In-depth knowledge of OWASP AI/ML vulnerabilities, including prompt injection, poisoning, adversarial attacks, and model extraction. • Understanding of common AI/ML model formats (pickle, TensorFlow, safetensors). • Strong background in cloud and container security, microservices architecture, and agile development practices. • Excellent problem-solving, analytical, and communication skills—able to work independently and as part of remote teams.