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 I/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 ATech Placement