Project Role : Security Architect
Project Role Description : Define the cloud security framework and architecture, ensuring it meets the business requirements and performance goals. Document the implementation of the cloud security controls and transition to cloud security-managed operations.
Must have skills : Secure AI
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Seeking a forward-thinking professional with an AI-first mindset to design, develop, and deploy enterprise-grade solutions using Generative and Agentic AI frameworks that drive innovation, efficiency, and business transformation.
AI Security Engineering Lead with 9+ years of experience in leading security design, governance, and operational hardening of enterprise-scale ML and GenAI platforms. Combines strong hands-on expertise with technical leadership, focusing on standardizing secure ML practices, driving AI risk governance, and guiding platform-level security decisions across multiple ML use cases
Roles & Responsibilities:
Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges, automate processes, and integrate intelligent insights into enterprise workflows for measurable impact.
Provide technical leadership for securing enterprise ML and GenAI platforms and pipelines across training, deployment, inference, and monitoring phases.
Define and enforce secure-by-design standards for ML platforms, pipelines, and end-to-end model lifecycle management.
Drive ML software and model supply-chain security for external models, datasets, and open-source components, including practices such as model signing, validation, and provenance tracking.
Guide the design and implementation of secure ML CI/CD, continuous training (CT), and continuous monitoring (CM), integrating security controls throughout pipeline automation.
Lead threat modeling and risk assessments for complex ML and GenAI architectures, identifying trust boundaries and high-risk attack surfaces.
Drive mitigation strategies for advanced threats including adversarial ML, model extraction, pipeline compromise, and trust-boundary violations.
Own AI/ML risk governance across bias, data drift, model drift, explainability, lineage, and compliance requirements.
Design secure inference architectures incorporating isolation, confidential computing, workload separation, and API protection mechanisms.
Provide oversight for ML platform security across managed services such as Azure ML, Amazon SageMaker, Vertex AI, and Databricks.
Define and enforce standards for container security, artifact security, registry protection, infrastructure-as-code (IaC) security, and policy-as-code enforcement for ML environments.
Oversee cloud, Kubernetes, and IaC security practices for ML workloads deployed on AWS, Azure, and GCP.
Establish and track KPIs such as percentage of ML pipelines with enforced security controls, model integrity checks, and monitoring coverage.
Review and approve ML security designs, pipeline implementations, and high-risk findings, acting as a quality gate for production readiness.
Act as an escalation point for critical ML and GenAI security issues and for risk acceptance or exception decisions.
Author and maintain ML security standards, governance artifacts, and operational playbooks to enable consistent adoption.
Mentor L9 engineers by reviewing security implementations, assessments, and remediation plans, and by guiding secure engineering practices.
Collaborate with engineering, platform, cloud, and compliance teams to continuously improve overall ML and GenAI security maturity across the organization.
Professional & Technical Skills:
Strong grasp of Generative and Agentic AI, prompt engineering, and AI evaluation frameworks. Ability to align AI capabilities with business objectives while ensuring scalability, responsible use, and tangible value realization. The candidate should be AI Native.
9+ years of experience in ML, MLOps, DevSecOps, or security engineering.
Deep understanding of ML system lifecycles, GenAI architectures, and enterprise deployment models.
Strong knowledge of ML supply-chain security and trusted AI asset management.
Strong expertise in ML specific threats, adversarial ML techniques, and AI governance controls.
Advanced experience with Python, CI/CD automation, Kubernetes, and cloud security architectures.
Experience assessing third-party AI provider and hosted model security risks
Strong understanding of AI platform observability and security telemetry requirements
Proven ability to design, review, and govern secure ML platforms at scale.
Strong communication and documentation skills for senior technical and leadership stakeholders.
Preferred certifications: Certified AI Security Specialist (CAISS), CISSP, ISACA Artificial Intelligence Security Management (AAISM) and Certified Offensive AI Security Professional (COASP).
Additional Information:
9 +years of relevant experience in the security design, governance, and operational hardening of enterprise scale ML and GenAI platforms
Employment Type: Full Time
Location: Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Gurugram (Gurgaon), Jaipur
A 15 year full time education is required. AI Powered Tech Talent
15 years full time education
