Senior Security AI Engineer

Job Summary

We are seeking a Senior Security AI Engineer to strengthen our cybersecurity posture across cloud, on-premises, and AI-enabled systems. This role is ideal for someone who combines deep technical security expertise with a forward-looking understanding of AI risks, governance, and secure deployment practices.

Key Responsibilities

• Security Architecture & Engineering Design, implement, and maintain security controls across cloud, on-premises, and hybrid environments.

• Lead threat modeling, secure design reviews, and architecture assessments for new and existing systems.

• Develop and maintain secure configurations, baselines, and hardening standards (OS, cloud, network, identity).

• Partner with DevOps and engineering teams to embed security into CI/CD pipelines.

• AI Security & Governance

• Evaluate and secure AI/ML systems, including LLMs, model pipelines, and data flows.
• Implement controls for model access, data privacy, prompt injection prevention, model poisoning, and supply chain risks.
• Establish AI governance practices aligned with NIST AI RMF, ISO/IEC 42001, and emerging regulatory requirements.
• Assess third-party AI tools for security and compliance risks.

• Threat Detection & Response

• Enhance detection capabilities across SIEM, EDR/XDR, cloud telemetry, and identity systems.
• Lead investigations into complex security incidents, including zero-day exploitation and advanced persistent threats.
• Develop playbooks, automation, and response workflows to reduce detection and response times.

• Risk Management & Compliance

• Align security controls with frameworks such as NIST CSF, NIST 800-53, CIS Controls, ISO 27001, PCI DSS, and FFIEC.
• Conduct risk assessments, gap analyses, and control maturity evaluations.
• Support audits, regulatory reviews, and evidence collection.

• Vulnerability & Attack Surface Management

• Lead vulnerability management, penetration testing coordination, and remediation prioritization.
• Implement and maintain CAASM/EASM tooling to identify shadow IT, unknown assets, and external exposures.
• Partner with infrastructure teams to ensure timely patching and configuration compliance.

• Collaboration & Leadership

• Mentor junior engineers and guide cross-functional teams on secure engineering practices.
• Communicate risks and recommendations clearly to technical and non-technical stakeholders.
• Drive continuous improvement of security processes, tooling, and automation.
• Produce architecture patterns and policies, and provide strategic guidance to engineering and leadership.

Required Skills & Experience

• Proven experience in the following areas:

• AI threat modeling, model governance, and data protection.
• Securing MLOps/LLMOps pipelines and implementing guardrails and monitoring.
• Leading AI red teaming engagements and integrating AI telemetry into security operations.
• Ensuring compliance with privacy and regulatory requirements.
• Using automation and tooling to monitor, detect, and respond to AI threats at scale.
• Understanding how models are trained, fine-tuned, evaluated, and deployed.
• Interpreting evaluation metrics and reasoning about model drift.
• Knowledge of base vs. fine-tuned models, zero-shot and few-shot behavior, embeddings and similarity search, and supervised vs. reinforcement learning.

• 7+ years of experience in cybersecurity engineering, architecture, or related roles.

• Strong knowledge of cloud security (AWS, Azure, or GCP).

• Expertise in identity and access management, network security, encryption, and secure coding practices.

• Hands-on experience with SIEM, EDR/XDR, vulnerability scanners, and cloud-native security tools.

• Deep experience in security architecture, cloud platforms, data security, and AI/ML systems.

• Background in identity and access management, network segmentation, application security, API hardening, securing containers and Kubernetes clusters, and protecting secrets and access tokens.

• Strong communication skills, cross-functional leadership ability, and familiarity with AI Risk and Governance Frameworks.

• Familiarity with AI/ML systems and defenses against threats including prompt injection, data poisoning, model extraction, and adversarial attacks.

Preferred Qualifications

• Experience securing LLMs, vector databases, model APIs, and AI agents.

• Knowledge of NIST AI RMF, ISO/IEC 42001, EU AI Act, or similar AI governance frameworks.

• Certifications such as CISSP, CCSP, OSCP, GIAC, or cloud security certifications.

• Experience with IaC security (Terraform, CloudFormation), container security, and Kubernetes.

• Familiarity with zero trust architectures and identity-centric security models.