Senior Technical Consultant – AI Security

AHEAD

. Deploy and operationalize AI security solutions in customer and enterprise environments .

Posted 11/10/2026full-timeRemote • United StatesSenior💰 $170,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in deploying and operationalizing AI security solutions, with a strong focus on cloud-native architectures, data integration, and security analytics workflows. Proven ability to communicate complex technical concepts to diverse stakeholders and mentor teams in implementing AI security best practices.

Highest-signal resume keywords

AI Security EngineeringCloud-Native Architecture DeploymentData Integration and Telemetry PipelinesTechnical Proposal DevelopmentScripting and Automation (Python, PowerShell, Bash)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Security SolutionsCloud-Native ArchitecturesData IntegrationTelemetry PipelinesSecurity Analytics WorkflowsAI Production PipelinesIntegration of APIs and WebhooksRed Teaming and Adversarial TestingModel Risk ScoringAnomaly Detection

Soft Skills

Problem-SolvingExecution MindsetCollaborationMentoringCommunication

Tools & Technologies

AWSAzureGCPREST APIsEvent-Driven WorkflowsLLMsVector DatabasesAI-Enabled Security ToolingAutomation WorkflowsInfrastructure as Code

Industry Keywords

AI GovernanceCompliance RequirementsSecurity ControlsOperational GuardrailsField Engineering PlaybooksRegulated EnvironmentsCI/CDAI Red TeamingAdversarial AI TestingMITRE ATLAS

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

Deploy and operationalize AI security solutions in customer and enterprise environments
Support technical proposals, statements of work, and solution architectures for AI security engagements
Translate business, operational, and AI security requirements into deployable architectures and implementation plans
Align solutions with AI governance, compliance requirements, and long-term platform strategy
Support onboarding, implementation, rollout, and optimization phases
Design and implement integrations across AI platforms, model registries, LLM gateways, vector databases, and AI-enabled security tooling
Build and configure cloud-native data ingestion, normalization, and enrichment pipelines for AI telemetry, model activity logs, and prompt/response data
Integrate APIs, webhooks, message queues, and automation workflows across AI operations, identity, cloud, and application ecosystems
Develop reusable deployment patterns, templates, and technical assets
Operationalize security controls and guardrails for AI systems
Secure non-human identities across AI and agentic workflows
Implement red teaming, adversarial testing, model risk scoring, and anomaly detection
Define data requirements, feedback loops, and operational guardrails for production AI systems
Implement secure automation for AI security monitoring, investigation, and response
Support resiliency, observability, performance, and scale requirements
Troubleshoot integration issues, deployment blockers, and production challenges
Improve reliability and maintainability through documentation, testing, monitoring, and standardized engineering practices
Lead collaboration across AI Engineering, Security Engineering, Data Science, Infrastructure, Sales, and product or customer teams
Communicate technical concepts to technical and non-technical stakeholders
Mentor engineers and consultants
Provide field feedback to influence platform roadmap, product direction, and architectural standards

Requirements

What you’ll need

7+ years of experience in security engineering, AI engineering, platform engineering, forward deployed engineering, solutions consulting, or related technical roles, with demonstrated progression to a principal or senior consulting level
Hands-on experience securing AI systems, LLM applications, or AI production pipelines
Experience deploying cloud-native architectures on at least one major cloud provider such as AWS, Azure, or GCP
Strong background in data integration, telemetry pipelines, normalization, and security analytics workflows applied to AI systems
Experience working directly with customers, executive stakeholders, or cross-functional delivery teams in implementation-focused environments
Proven experience developing technical proposals, statements of work, and scoping documents for customer engagements
Strong understanding of AI security risks, including prompt injection, model theft, data poisoning, adversarial attacks, and agentic AI risks, alongside foundational cloud, identity, and endpoint security
Experience with scripting and automation using Python, PowerShell, or Bash
Familiarity with REST APIs, webhooks, and event-driven workflows
Working knowledge of LLMs, vector search, retrieval-augmented generation, model evaluation, and AI assistants or agents
Strong problem-solving skills, execution mindset, and ability to operate effectively in ambiguous, fast-moving environments
Bachelor’s degree in Computer Science, Information Security, Engineering, or equivalent practical experience
Preferred: Experience with OpenAI models, Anthropic models, AI red teaming, adversarial AI testing, AI governance frameworks, MITRE ATLAS, regulated environments, infrastructure as code, CI/CD, AI production delivery, reusable implementation frameworks, field engineering playbooks, and relevant cloud, security, or AI certifications

Benefits

Comp & perks

Medical, Dental, and Vision Insurance
401(k)
Paid company holidays
Paid time off
Paid parental and caregiver leave
Cross-department training and development
Sponsored certifications and credentials for continued learning
AI application and resume review opt-out without penalty
Interview recording and transcription opt-out without penalty