Senior Security Engineer, Artificial Intelligence (W2) || Irving, TX (5 days onsite)

Hi,

Hope you are doing well!

I’m Shikha Dixit, Lead Recruiter at Siri Info Solutions. I’ve been reviewing your background in [Security Engineer, Artificial Intelligence, LLM, Cybersecurity, Cloud, Python, API etc.]. Your profile stands out as an excellent match for a “Senior Security Engineer, Artificial Intelligence” opening I have in Irving, TX. This is a high-priority, 5-day on-site role with one of our key clients. Given your experience, I’d love to discuss the details and see if this aligns with your career goals. Are you open to a quick 5-minute chat?

 

Role: Senior Security Engineer, Artificial Intelligence (W2)

Duration: 12 months

Location: Irving, TX (5 days onsite)

 

Job Description:

Role Summary
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, Google Cloud Platform
• 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 Google Cloud Platform
• 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

 

Best Regards,

Shikha Dixit| Email: | LinkedIn:

Lead Recruiter

Siri InfoSolutions Inc, 3 Ethel Rd, Suite # 302, Edison NJ 08817.