Lead MLSecOps Security Engineer

We are seeking a Lead MLSecOps Security Engineer.

What You’ll Do

• Design, implement, and maintain secure ML pipelines for AI/ML model evaluation, validation, deployment, and inference.
• Assess and mitigate security risks throughout the ML lifecycle, including data ingestion, model storage, and deployment.
• Evaluate, secure, and govern AI coding agents, autonomous agents, and agentic workflows used throughout the software development lifecycle.
• Define and implement security guardrails for AI-assisted software development platforms, agent orchestration frameworks, and autonomous development pipelines.
• Develop and operationalize an Agentic Development Lifecycle (ADLC) framework that incorporates security requirements, threat modeling, testing, deployment governance, and continuous security monitoring.
• Evaluate AI Security Posture Management (AI-SPM) capabilities and establish processes for identifying, categorizing, prioritizing, and remediating risks associated with AI assets, agents, prompts, datasets, and AI-enabled applications.
• Assess emerging threats against frontier AI models and agentic systems and recommend preventative and detective security controls to reduce enterprise risk.
• Develop and maintain code for AI/ML pipelines using Python and CICD, ensuring robust security controls and compliance with best practices.
• Institutionalize security scanning of AI/ML models in line with shift left strategy; interpret results and remediate identified issues.
• Evaluate and optimize model inference deployment strategies, balancing security, performance, and resource utilization.
• Stay current on top vulnerabilities affecting Machine Learning Models, Large Language Models (LLMs), and AI agents, such as prompt injection, data poisoning, model theft, and adversarial attacks.
• Collaborate with data scientists, ML engineers, and security teams to drive adoption of secure ML practices.
• Establish strong partnership with key stakeholders in technology and product organizations.
• Perform other duties as required.

Experience You'll Need

• Hands-on experience with MLOps pipelines and model deployment tools (e.g., Kubeflow, MLflow, SageMaker).
• Strong programming skills in Python and CICD for automation and pipeline development.
• Hands-on experience with major AI coding assistants and coding agents such as GitHub Copilot, Microsoft Copilot, Cursor, Claude Code, Windsurf, or similar AI-assisted development platforms.
• Experience using AI-driven development techniques across multiple programming languages including Python, Java, JavaScript, C#, .NET, Go, or similar technologies.
• Strong understanding of agentic architectures, AI agents, autonomous workflows, Retrieval-Augmented Generation (RAG), and associated security considerations.
• Experience assessing Agent Sandboxes, agent runtime environments, agent-to-tool communications, and agent execution workflows.
• Understanding of security limitations and control mechanisms governing agent behavior, including permissions, approval workflows, runtime controls, and data protection guardrails.
• Ability to identify, categorize, prioritize, and operationalize risks associated with AI assets, models, prompts, datasets, agents, and AI-enabled applications.
• Deep understanding of Agentic Development Lifecycle (ADLC) principles and integration of security controls throughout planning, development, testing, deployment, and operations.
• Knowledge of frontier AI model cybersecurity programs and the ability to reason about layered controls that mitigate AI-driven cyber attacks, adversarial ML threats, model and agent abuse.
• Familiarity with structured (SQL, data warehouses) and unstructured (object storage, NoSQL) data systems.
• Familiarity with Databricks
• Experience with ML security tools for model scanning and vulnerability assessment.
• Knowledge of top OWASP AI/ML vulnerabilities, including:
• Prompt injection
• Data and model poisoning
• Model extraction and inversion
• Adversarial example attacks
• Supply chain risks in ML components
• Strong communication skills and ability to document and explain Cybersecurity and AI/ML security controls to technical and non-technical stakeholders.
• Understanding of AL/ML model formats such as pickle, tensorflow, safetensors
• Experience in rolling out model scanning solution as part of model development.
• Understanding CI/CD pipelines covering source control, integration, and deployment (ex: Bitbucket, Jenkins, JIRA, Artifactory, Nexus, SonarQube, git, Snyk scanner).
• Previous software engineering/architecture experience (Java, C#, .Net, JavaScript, Python) preferred.
• Strong analytical/problem solving skills and cross functional knowledge across multiple development and security disciplines.
• Experience with development of RESTful web services preferred.
• Understanding of advanced iterative Agile, Cloud and Container Security, GenAI Security
• Exceptional problem-solving skill
• Excellent communication and presentation skills
• Ability to be a good team player as part of remote teams
• Self-motivated with positive attitude
• Should be able to work independently.

Qualifications

• Bachelor's degree in computer science, Information / Cyber Security, Computer Systems Engineering, Computer Information Systems or equivalent education and experience required
• Eight years or more experience in various IT or cybersecurity roles, with five or more years of experience specifically in software engineering roles.
• Deep knowledge and understanding of AI/ML and Agentic Security and related risks
• Candidate should be very thorough in internet technologies and highly versed with web development best practices.
• Strong analytical/problem solving skills and cross functional knowledge across multiple development and security disciplines.
• Ability to communicate security-related concepts to a broad range of technical and non-technical stakeholders.
• Understanding of advanced iterative Agile and container & cloud security
• Familiarity with micro-services architecture and Design Patterns
• Excellent analytic skills, including qualitative and quantitative data analysis to support and defend data-driven decision-making regarding system threats, vulnerabilities, and risk
• Any of the following are a plus but not necessary: CEH, CISSP, CSSLP, GCIA, GPEN, GWAPT