Senior Forward Deployed Engineer, AI Security

Responsibilities

Customer and Stakeholder Delivery

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

• Support development 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

• Support engagement leaders 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 Leadership

• Lead 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

• 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

Required Qualifications

• 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

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