Principal Technical Consultant - AI Security

Responsibilities

Customer and Stakeholder Delivery

Serve as the senior technical lead for deploying and operationalizing AI security solutions in customer or enterprise environments

Develop technical proposals, statements of work, and solution architectures that scope AI security engagements and support business development efforts

Translate business, operational, and AI security requirements into deployable architectures and implementation plans

Partner with internal and external stakeholders to ensure solutions are aligned to AI governance, compliance requirements, and long-term platform strategy

Act as a trusted advisor and technical point of contact during onboarding, implementation, rollout, and optimization phases

Solution Implementation and Integration

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 supporting AI systems

Develop reusable deployment patterns, templates, and technical assets to accelerate future AI security implementations

AI Security Operations

Operationalize security controls and guardrails for AI systems, including prompt injection defense, model access controls, data leakage prevention, and agentic workflow monitoring

Secure non-human identities (NHI), including service accounts, API keys, tokens, and autonomous agent credentials, across AI and agentic workflows

Work with AI, detection engineering, and data teams to implement red teaming, adversarial testing, model risk scoring, and anomaly detection for AI systems

Help define data requirements, feedback loops, and operational guardrails needed to secure AI systems in production environments

Ensure deployed solutions are practical, measurable, and aligned to AI governance and risk objectives

Automation and Reliability

Implement secure and governed automation for AI security monitoring, investigation, and response use cases across heterogeneous environments

Support resiliency, observability, performance, and scale requirements for deployed AI security solutions

Troubleshoot integration issues, deployment blockers, and production challenges in partnership with AI, platform, cloud, and security teams

Improve reliability and maintainability through documentation, testing, monitoring, and standardized engineering practices

Cross-Functional Teaming

Work cross-team collaboration across AI Engineering, Security Engineering, Data Science, Infrastructure, Sales, and product or customer teams

Communicate technical concepts clearly to both technical and non-technical stakeholders, including executive audiences

Mentor and develop engineers and consultants, contributing to best practices for AI security implementation, delivery, and technical solution design

Provide field feedback to influence platform roadmap, product direction, and architectural standards

Support the practice in pre-sales activities, proposal development, and client presentations

Required Qualifications

10+ 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
Ability to lead technical engagements, drive execution, and influence outcomes without direct authority
Demonstrated ability to enable cross-team collaboration across engineering, security, data science, and business stakeholders

Technical Skills

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 tools such as Python, PowerShell, or Bash
Familiarity with APIs and integration patterns including REST, webhooks, and event-driven workflows
Working knowledge of AI concepts including 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

Preferred Qualifications

Experience with implementation and integration of OpenAI models, specifically Daybreak Blue and/or Red.
Experience with implementation and integration of Anthropic models.
Experience with AI red teaming, adversarial AI testing, or AI governance frameworks such as the NIST AI RMF, ISO/IEC 42001, or the OWASP Top 10 for LLM Applications
Familiarity with the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) knowledge base for AI-specific attack techniques and mitigations
Experience deploying solutions in regulated or compliance-sensitive environments
Familiarity with infrastructure as code, CI/CD workflows, and AI production delivery practices
Experience building reusable implementation frameworks or field engineering playbooks
Relevant certifications are a plus, including cloud, security, or AI-specific certifications

Education

Bachelor’s degree in computer science, Information Security, Engineering, or equivalent practical experience