The Senior Technical Consultant (Forward Deployed Engineer) for AI Security is a senior, customer-facing technical engineer responsible for deploying, integrating, and operationalizing AI security capabilities in real-world customer and enterprise environments, with a focus on securing AI systems, LLM applications, and agentic workflows. This role works at the intersection of engineering, AI, security, data, and customer success to translate complex requirements into scalable, production-ready solutions.
This individual partners closely with security leads, AI engineers, data scientists, platform teams, and infrastructure stakeholders to implement AI security controls, secure AI development pipelines, integrate security tooling for AI systems, and accelerate time to value. The ideal candidate brings 7+ years of security experience and combines deep technical expertise in AI systems, model and data security, and cloud platforms with proven experience developing technical proposals and statements of work, leading cross-team collaboration, and driving customer-facing delivery at a principal level.
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
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 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
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
Develop technical strategies and joint solutions with systems integrator partners, providing architectural guidance, demos, proofs of concept, and deal support to grow Snowflake offerings and partner mindshare.
Own enterprise customer technical relationships and deliver AI solutions from architecture through launch, building integrations, advising on agentic AI roadmaps, and shaping the platform through customer feedback.
Guide enterprise customers from solution discovery through production deployment by designing agentic AI workflows, context models, and data integrations that turn complex data into actionable intelligence.
Lead customer-facing technical engagements from discovery through production, connecting enterprise environments with product and engineering teams and delivering proofs of value for AI and data systems.
Own post-sale technical deployments for enterprise customers, writing production code in their data environments, driving adoption and measurable value, and shaping the forward deployed engineering playbook.
