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Alfa AI · 7 hours ago

AI Security Engineer

United States

Full-time

Remote

Senior Level

6+ years exp

Alfa AI is focused on AI Security Engineering, and they are seeking an AI Security Engineer to design and implement secure architectures for Large Language Model (LLM) and Agentic AI ecosystems. The role involves ensuring robust data protection, model governance, and compliance while bridging security architecture with AI engineering and risk governance.

Artificial Intelligence (AI)B2BSaaS

Responsibilities

Engineer secure environments for enterprise LLM platforms (ChatGPT, Claude, Gemini, Azure OpenAI)

Design zero-trust architectures for AI ecosystems, including MCP servers/clients and agentic workflows

Secure LLM model lifecycle: training, fine-tuning, evaluation, deployment, inference endpoints

Define agent-to-agent (A2A) trust boundaries, cryptographic trust chains, message integrity controls

Establish guardrails for Retrieval-Augmented Generation (RAG), tool use, plugins, function calling, enterprise embeddings, contextual memory

Implement runtime sandboxing, prompt firewalling, data path isolation, interaction filtering

Apply frameworks: NIST AI RMF, MAESTRO, OWASP Top 10 for LLM & Agentic AI, MITRE ATLAS, ISO/IEC 23894 & 42001, Google SAIF, Microsoft Responsible AI Standard

Establish model governance, evaluation criteria, audit logs, chain-of-thought protection, policy configuration

Conduct threat modeling using: LLM-specific, Agentic AI Self-Propagation & Tool Abuse, RAG Architecture Security, A2A Trust Exploitation, MCP Supply-Chain & Man-in-the-Middle models

Define adversarial defenses: prompt injection mitigation, jailbreak prevention, indirect prompt poisoning, model exfiltration protection, data poisoning countermeasures, model inversion & membership inference prevention

Design secure Azure OpenAI & Azure AI Foundry deployments: private endpoints, VNet isolation, mTLS/encryption, model filtering, enterprise data security

Secure Gemini Enterprise & Google LM Notebooks: VPC Service Controls, IAM conditional access, DLP, context filtering, confidential computing

Govern MCP tools, input/output sanitization, policy-guarded capability authorization

Define secure orchestration and oversight for multi-agent LLM systems: autonomy limits, escalation rules, tool use governance

Implement Secure MLOps: dataset lineage, provenance, quality checks, differential privacy, secure gradient computation, adversarial training, signed/documented model artifacts

Secure confidential training data, prevent leakage to public models

Enable runtime protection, anomaly detection, exploit signal monitoring

Build AI-specific incident playbooks: hallucination incidents, governance policy drift, unauthorized agent actions, emergent harmful behavior

Qualification

AI Security EngineeringGenerative AI SecurityZero-trust ArchitecturesSecure MLOpsPythonTypeScript/Node.jsTerraform/IaCAI MonitoringSoft Skills

Required

6–10 years in cybersecurity, including 2+ years in AI/ML security or LLM platform engineering

Deep understanding of generative AI security: LLM jailbreak defense, guardrails engineering, AI alignment, content filtering, advanced prompt-level security

Knowledge of LLM tool ecosystems (functions, plugins, RAG)

Security configurations for ChatGPT Enterprise, Claude Enterprise, Gemini Enterprise, Google LM Notebooks, OpenAI on Azure, Azure AI Foundry

Zero-trust architectures, KMS/HSM/secrets management, API/function calling security, encryption controls, network/IAM/private routing, DSPM, CASB, CSPM, AIRS tools