• 401(k)
• Employee discounts

Core Applied AI Engineer – Agentic AI & Security (W2 POSITION ONLY, NO C2C OR 1099)

• End-to-End Workflow Automation: Design and deploy AI agents that execute complex business processes across enterprise APIs, databases, SaaS platforms, and MCP/tool servers.
• Reliability & Optimization: Utilize context engineering and harness engineering to optimize agent performance, control, observability, and traceability.
• Cross-Functional Collaboration: Partner with Product, Data, Platform, and Security teams to embed AI capabilities seamlessly into enterprise infrastructure.

• Security Controls & Guardrails: Design robust guardrails governing agent tool invocation, API access, data retrieval, and autonomous decision-making.
• Threat Mitigation: Protect systems against agent-specific vulnerabilities, including direct/indirect prompt injection, context poisoning, tool abuse, privilege escalation, and data exfiltration.
• Identity & Access Management: Implement least-privilege access models, service identities, OAuth/OIDC, RBAC/ABAC, and secure secrets management.
• Human-in-the-Loop: Establish automated and manual checkpoints requiring approval where business or security risk thresholds demand it.

• Telemetry & Auditing: Implement tracing and monitoring frameworks to log agent reasoning, decision paths, tool calls, and downstream actions.
• Adversarial Testing: Establish rigorous evaluation pipelines to test agent behavior, security boundaries, and failure modes under adversarial scenarios.

• Engineering Experience: Proven track record of building and deploying production-grade agentic AI systems or autonomous workflows.
• Core AI Expertise: Deep proficiency in context engineering, harness engineering, reasoning loops, and agent orchestration.
• Security Background: Strong foundation in application, API, data, or cloud security, with specific experience securing AI architectures.
• Enterprise Security Knowledge: Familiarity with LLM threat vectors (prompt injection, excessive agency, data leakage) alongside traditional IAM, least-privilege, and policy enforcement models.
• Analytical & Communication Skills: Excellent problem-solving capabilities with the ability to bridge technical and business requirements effectively.

• Experience with Model Context Protocol (MCP), multi-agent architectures, or agentic frameworks.
• Background in AI security frameworks, threat modeling, red teaming, or adversarial testing.
• Proficiency in cloud security (Azure, AWS, or Google Cloud Platform) and large enterprise data platforms.
• Experience integrating AI agents with transactional systems such as ERP, CRM, or commerce platforms.

• Determining and enforcing strict operational boundaries and permissions for an AI agent.
• Securing an agent's context, memory, tool servers, and execution environment.
• Detecting and mitigating prompt injection and context manipulation.
• Designing audit trails and telemetry for agent reasoning and tool execution.
• Constructing adversarial testing frameworks to validate agent reliability.