A production engineer embedded inside a client's enterprise to secure, govern, and sustain AI deployments, delivering end-to-end security design, tooling, and governance within 90-day delivery pods.
Responsibilities
- Support AI security architecture and threat modeling for production agentic deployments across complex multi-stakeholder client environments, including LLM systems, multi-agent pipelines, RAG architectures, and MLOps infrastructure, owning the full security design from assessment through hardened deployment.
- Deliver hands-on security engineering using agentic coding tools as the primary build environment: develop AI powered detection systems, automated threat response tooling, security assessment frameworks, and governance automation using Claude Code, Cursor, or GitHub Copilot in daily delivery practice.
- Support AI specific threat surface management at program scale: OWASP LLM Top 10 controls, prompt injection hardening, model extraction prevention, adversarial input defenses, and AI supply chain security across concurrent client workstreams.
- Architect and govern AI security controls across the enterprise stack: identity and access for AI systems, data pipeline security, model serving security, and multi-system integration risk across cloud platforms (AWS, Azure, or GCP).
- Assist with AI governance framework implementation: EU AI Act, NIST AI RMF, and model risk management applied to live production systems, not theoretical compliance exercises.
- Shape AI reinvention security strategy for client CISO and CTO: build risk-adjusted investment cases, security architecture roadmaps, and AI governance operating models aligned to commercial outcomes.
- Define and publish reusable security patterns, playbooks, and accelerators that scale across multiple client engagements and grow the Secure AI practice.
- Contribute to and support architecture design sessions, threat modeling workshops, and code-with sessions with client engineering and security leadership teams.
Requirements
- Minimum of 3 years of engineering experience in production environments with depth in at least one cybersecurity area: AppSec, SecOps / detection engineering, cloud security, IAM, offensive security / penetration testing, or GRC.
- Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in production environments; theoretical familiarity does not qualify.
- Minimum 3 years working with cloud platform security fundamentals across at least one provider (AWS, Azure, or GCP): IAM, network security, secrets management, and AI service security configurations.
- Bachelor's degree or equivalent work experience (minimum 12 years). If using an Associate’s Degree, a minimum of 6 years of work experience is required.
Technologies
- Claude Code
- Cursor
- GitHub Copilot
- AWS
- Azure
- GCP
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Life insurance
- Long-term disability insurance
- 401(k) plan
- Bonus opportunities
- Paid holidays
- Paid time off
Requesting an Accommodation
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