Core Applied AI Engineer Agentic AI & Security

Benefits

401(k)

Employee discounts

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

Introduction

Our client is seeking a Core Applied AI Engineer to architect and deploy end-to-end agentic workflows that automate critical business processes. The ideal candidate pairs deep expertise in core applied AI—including reasoning, orchestration, context engineering, and harness engineering—with rigorous security engineering.

This role moves far beyond traditional RAG or conversational chatbots. You will build secure, autonomous agents capable of reasoning, decision-making, API/tool invocation, and safe integration within complex enterprise environments.

Responsibilities

Agentic Architecture & Orchestration

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.

Agentic Security & Governance

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.

Observability, Testing & Evaluation

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.

Requirements

Required Skills

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.

Preferred Skills

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.

Key Interview Discussion Areas

Candidates for this role should be prepared to discuss hands-on experience addressing:

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.

This is a remote position.