AI Security Architect - Applied AI Security

ByLabs; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

ByLabs in San Francisco seeks an AI security architect to design and operate the security layer for AI-enabled systems spanning trading, risk, compliance, and customer platforms.

You will define trust boundaries, authorization models, and least-privilege access for multi-agent AI ecosystems, and lead threat modeling, red team exercises, and secure development lifecycles tailored to AI workloads.

Qualifications

• Minimum 6+ years in security engineering or architecture.
• At least 2+ years focused on ML/AI systems.
• Deep understanding of LLM internals and attack surfaces.
• Hands-on experience with prompt injection, jailbreak research, or adversarial ML.
• Experience securing production AI systems, not just papers.

Responsibilities

• Design end-to-end security architecture for AI stack and workflows.
• Lead AI threat modeling and red team exercises; evolve automation platforms.
• Define security review processes for AI features and vendor integrations.
• Architect TEE-based deployments and attestation flows for secure inference.

Skills

Security architecture

ML/AI systems

Threat modeling

Red team

Prompt injection

Tools

LangChain

CrewAI

OpenAI Assistants

Anthropic's tool use

MCP

Job description

As we deploy AI agents across trading, risk, compliance, and customer systems, we're building the security architecture that keeps those systems trustworthy at scale.

This isn't a compliance checkbox role. You'll architect the security layer for systems where a compromised AI agent can authorize transactions, move funds, or access millions of user records. The threat model includes nation-state actors, sophisticated financial fraud, and the exact class of autonomous AI attack campaigns that are actively targeting financial infrastructure today.

You'll work directly with Group Risk & Security leadership to define how we build, deploy, and operate AI safely — from model selection to runtime monitoring to red team automation.

Key Responsibilities

AI System Security Architecture

• Design end-to-end security architecture for AI stack: LLM inference pipelines, AI agents, MCP-based tool orchestration, and agentic workflows
• Define trust boundaries, authorization models, and least-privilege principles for multi-agent systems
• Architect the AI Security Gateway: prompt/response inspection, rate limiting, anomaly detection, circuit breaking across all AI-powered products

Threat Modeling & Red Teaming

• Build and operate an AI-specific threat model covering prompt injection, jailbreak, model inversion, supply chain poisoning, and autonomous agent misuse
• Lead red team exercises against our AI deployments; evolve our Red vs Blue automation platform (currently in production, LLM-driven)
• Define detection signatures for AI-assisted attacks — we've seen the Hermes/Strix/Cairn class of autonomous attack campaigns firsthand

Secure AI Development Lifecycle (AI-SDL)

• Define security review processes for AI features: model selection, fine-tuning pipelines, RAG system design, tool call authorization
• Evaluate third-party AI vendors and integrations (model APIs, vector DBs, agent frameworks) for security posture
• Work with product and infra teams to embed security requirements from design through deployment

TEE & Confidential Computing

• Architect TEE-based deployments for sensitive AI inference (AMD SEV-SNP, Nvidia Confidential Computing)
• Design attestation flows, measurement coverage, and post-deployment verification — with the rigor of a Trail of Bits-level audit

Required

• 6+ years in security engineering or architecture; 2+ years focused on ML/AI systems
• Deep understanding of LLM internals: pretraining, RLHF, inference, safety alignment mechanisms — and how each layer can be attacked
• Hands-on experience with prompt injection, jailbreak research, or adversarial ML (published work, CTF, or production red team)
• Experience building or evaluating security for production AI systems (not just reading papers)
• Familiarity with AI agent frameworks: LangChain, CrewAI, OpenAI Assistants, Anthropic's tool use, MCP

Strong Signal

• Published red team research, CVEs in AI tooling, or presented at Black Hat / DEF CON / IEEE S&P on AI security topics
• Experience with TEE deployment in production (SGX/TDX/SEV-SNP)
• Background in financial services, crypto, or other high-stakes transaction systems
• Contributed to AI safety frameworks (responsible disclosure, model cards, safety evals)

Why ByLabs

• The threat surface is real. AI-powered autonomous attack campaigns are already hitting financial infrastructure. You won't be building defenses for hypothetical future threats — the Cairn-class agent that chains SQLi → RCE → AWS secrets extraction in one autonomous run is already operational against our industry.
• The scope is unusually large. We run AI across risk scoring, fraud detection, customer support, compliance screening, and internal tooling. The security architecture you design will cover all of it.
• You'll ship, not just advise. This role produces systems, not slide decks. Red team automation platform, AI security gateway, TEE attestation flows — built and running in production.
• Becoming a product. Our security capabilities are on a path to commercialization. The AI security architecture you build here will eventually be offered as a product to other exchanges and financial institutions.