Job description

Job Description: Our client is seeking a Senior Security Engineer (AI) to join their team. You design, build, and run security controls for artificial intelligence systems across the full lifecycle. You secure model development, training data, pipelines, APIs, and AI-enabled applications. You work with product, engineering, data science, and compliance teams to reduce exposure from model misuse, data leakage, supply chain threats, and adversarial attacks. You deliver measurable improvements in AI governance, detection, and incident response. Key Responsibilities: Secure the AI and ML lifecycle Define security requirements for model development, training, evaluation, deployment, and monitoring Threat model AI systems and AI features in products, including abuse cases and misuse scenarios Establish secure-by-design patterns for model endpoints, prompts, RAG pipelines, and agent workflows Validate controls for model access, rate limiting, tenant isolation, and secrets management Protect data used by AI: Classify and control training data, fine-tuning data, prompts, and retrieved context Implement guardrails for sensitive data exposure, including PII and PCI data Define retention, deletion, and lineage requirements for AI datasets and outputs Partner with Privacy and Legal on data handling, regulatory expectations, and third-party data use Secure AI infrastructure and supply chain: Harden AI platforms, GPU and container workloads, model registries, and artifact stores Assess risks in third-party models, libraries, embeddings, and model hosting services Define integrity controls for model artifacts, evaluation sets, and pipeline automation Build CI and CD checks for AI assets, including scanning, signing, and policy enforcement Detection, monitoring, and response for AI threats: Build logging standards for model requests, responses, tool calls, and retrieval events Create detections for prompt injection, data exfiltration attempts, model extraction signals, and anomalous usage Develop incident response playbooks for AI events, including containment and rollback plans Run security testing for AI features, including red teaming and structured adversarial testing Governance and program delivery: Create practical AI security standards, patterns, and reference architectures Define KPIs such as reduction in sensitive output leakage, time to detect misuse, and policy coverage Lead risk reviews for new AI features and vendor assessments for AI services Train engineering and data science teams on secure AI patterns and common attack paths Tools and Technologies You Might Use: Cloud: AWS, Azure, GCP Containers: Kubernetes, Docker DevSecOps: GitHub Actions, GitLab CI, Azure DevOps, Terraform Security: SIEM, EDR, WAF, API gateways, secrets managers AI stack: model gateways, vector databases, model registries, ML pipelines Examples of Work and Technical Scope: Secure an LLM gateway with authentication, authorization, quotas, content filtering, and audit logging Add prompt injection defenses for an agent that uses tools like web search and internal APIs Implement retrieval filtering, context redaction, and output scanning for a RAG application Build model artifact signing and verification into the release pipeline Create detections in SIEM for abnormal model usage, including model scraping patterns Required Qualifications: Bachelor’s degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field 5+ years in security engineering, application security, cloud security, or detection engineering Experience securing LLM-based applications, RAG systems, or agentic workflows Familiarity with adversarial ML concepts, such as prompt injection, model inversion, and model extraction Experience with one or more cloud platforms, AWS, Azure, or GCP Experience with Kubernetes and container security Hands-on experience with at least one programming language, Python preferred Strong understanding of AI, LLMs, API security, identity, secrets management, and cloud controls Experience building security controls into CI and CD pipelines Proven ability to lead cross-functional security work with engineering and product teams Effectively communicate complex technical concepts to both technical and non-technical stakeholders Effectively communicate to leadership and know when to escalate with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions Proven leadership capabilities with the ability to influence and drive change Preferred Qualifications: Master’s degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field AI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy) Experience with security telemetry and detections in SIEM or EDR platforms Salary/Rate: $70-$95/HR (depends on experience level). This is a contract position with candidates expected to work 40 hours/ week.

Requirements

Must-haves

Secure the AI and ML lifecycle
Define security requirements for model development, training, evaluation, deployment, and monitoring
Threat model AI systems and AI features in products, including abuse cases and misuse scenarios
Establish secure-by-design patterns for model endpoints, prompts, RAG pipelines, and agent workflows
Validate controls for model access, rate limiting, tenant isolation, and secrets management
Classify and control training data, fine-tuning data, prompts, and retrieved context
Implement guardrails for sensitive data exposure, including PII and PCI data
Define retention, deletion, and lineage requirements for AI datasets and outputs
Partner with Privacy and Legal on data handling, regulatory expectations, and third-party data use
Harden AI platforms, GPU and container workloads, model registries, and artifact stores
Assess risks in third-party models, libraries, embeddings, and model hosting services
Define integrity controls for model artifacts, evaluation sets, and pipeline automation
Build CI and CD checks for AI assets, including scanning, signing, and policy enforcement
Detection, monitoring, and response for AI threats:
Build logging standards for model requests, responses, tool calls, and retrieval events
Create detections for prompt injection, data exfiltration attempts, model extraction signals, and anomalous usage
Develop incident response playbooks for AI events, including containment and rollback plans
Run security testing for AI features, including red teaming and structured adversarial testing
Create practical AI security standards, patterns, and reference architectures

Nice-to-haves

Hands-on experience with at least one programming language, Python preferred
Master’s degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field
AI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy)
Experience with security telemetry and detections in SIEM or EDR platforms
Salary/Rate: $70-$95/HR (depends on experience level). This is a contract position with candidates expected to work 40 hours/ week.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlawsazuregcpkubernetesdockerapillmpythonmachine learning

Platform: careers-page
Effort: medium