AI Cybersecurity Engineer

Location: Oaks

We are seeking an AI Cybersecurity Engineer to serve as a technical security lead and architect interfacing with our company’s various AI initiatives. This strategic role combines deep expertise in artificial intelligence, machine learning, and cybersecurity to design, architect, and lead the development of secure, scalable AI-driven security platforms that protect our organization against evolving AI-powered  this position, you will serve as the technical visionary and hands-on architect responsible for defining security strategies for AI systems, engaging with cross-functional engineering teams, mentoring security professionals, and partnering with senior stakeholders across security, technology, risk, and compliance organizations.

You will balance cutting-edge AI/ML engineering with robust cybersecurity leadership to establish security-by-design principles across our AI ecosystem while ensuring our defenses evolve at the speed of emerging threats.

What You Will Do:

Strategic Architecture & Technical Leadership Design and architect enterprise-grade, secure AI security platforms that protect ML models, training pipelines, inference systems, and AI-driven applications from sophisticated adversarial attacks.

Define and drive the technical vision and security roadmap for all AI/ML initiatives across the organization, embedding security into the complete AI lifecycle from development through deployment and monitoring.

Lead architectural reviews and provide authoritative technical guidance on security architecture patterns, threat models, and risk mitigation strategies for AI systems.

Establish security standards and frameworks for AI development, incorporating OWASP LLM Top 10, MITRE ATLAS, NIST AI Risk Management Framework, and other industry best practices.

AI/ML Security Engineering & Implementation Develop security controls for AI model training, validation, deployment, and monitoring including input/output filtering, model integrity validation, and behavioral anomaly detection.

Implement data security and privacy controls across AI workflows including sensitive data detection, data loss prevention for AI prompts and responses, and confidential computing techniques.

Build automated security testing frameworks for continuous validation of AI model security posture and detection of adversarial attack patterns.

Engineer AI-powered security detection systems leveraging machine learning for threat hunting, anomaly detection, and behavioral analytics.

Cross-Functional Collaboration & Stakeholder Management Communicate complex technical concepts to non-technical executives and business leaders, translating security risks into business impact and strategic recommendations.

Serve as the technical authority and trusted advisor on AI security matters for senior leadership including CISO and CTO.Governance, Risk & Compliance Develop and enforce AI security governance policies, standards, and guidelines that ensure ethical, safe, and compliant use of AI across the enterprise.

Establish AI model governance frameworks addressing model validation, bias detection, explainability requirements, and audit trails.

Implement continuous monitoring and observability for AI systems to detect model drift, performance degradation, and security anomalies in real-time.

What we need from you:

Bachelor's degree in Computer Science, Cybersecurity, Information Security, Software Engineering, or related technical field preferred.

Advanced coursework or specialization in artificial intelligence, machine learning, cryptography, or secure systems design.

A minimum or 10 years of progressive experience in cybersecurity engineering , with at least 2+ years focused on AI/ML security, application security, or security architecture.

Deep expertise in AI/ML security principles including adversarial machine learning, model security, data poisoning detection, and prompt injection defense.

Expert-level knowledge of AI/ML frameworks and platforms (Tensor Flow, PyTorch, scikit-learn, Hugging Face) and their security implications.

Extensive experience with cloud security architectures on AWS, Azure, OCI, or GCP, specifically securing AI/ML workloads in cloud environments.

Strong proficiency in programming languages including Python (primary), Java, C#, Go, or similar with emphasis on secure coding practices.

Proven experience designing and implementing security for LLMs and generative AI systems including RAG architectures, vector databases, and agent frameworks.

Demonstrated ability to securely integrate AI/ML solutions with existing legacy applications (e.g., ERP, CRM, mainframe, or on-prem systems) using modern integration patterns (APIs, gateways, middleware, or RPA), while enforcing enterprise security controls such as RBAC, encryption, logging, and compliance with data governance standards.

Hands-on expertise with MLOps/MLSecOps tool chains, CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure-as-code.

Deep understanding of security frameworks and…