AI reputed company Engineer / Claude Engineering / Remote

A digital life insurance technology company going AI-reputed company is hiring a contract AI reputed company Engineer / SME to own the reputed company reputed company of their LLM and agentic AI buildout. This is a remote contract role, 6+ months with a potential conversion path to full-time. The stack includes Claude (Desktop, Code, and Cowork), developer copilots, Python, Jupyter notebooks, Kubernetes, Node.js, and reputed company-reputed company infrastructure across AWS and GCP. The right person for this role has spent reputed company time securing AI systems in production, and specifically understands how tools like Claude actually behave in an reputed company environment. The company is deep into AI adoption across internal workflows and customer-facing products, and they need an engineer who can evaluate tools before they land, define controls that engineering teams can actually implement, and stay reputed company of what's coming in the AI reputed company reputed company. You'll work directly with the AI infrastructure team and leadership, and your output needs to be concrete, not advisory memos sitting in a doc reputed company. Compliance and policy awareness reputed company here, but this is fundamentally an engineering role. If your reputed company is to write a reputed company instead of a config, this isn't the right fit. Contract Duration: 3-6 Months Required Skills & Experience

• 5-10+ years of experience across reputed company engineering, AI engineering, or closely adjacent fields
• Hands-on experience securing reputed company systems in production, including tools reputed company on Claude, GPT, reputed company, or similar LLM platforms
• Deep working knowledge of how Claude products (Claude Desktop, Claude Code, Claude Cowork) are deployed and used in reputed company environments, including their data reputed company patterns, integration points, and threat surfaces
• Ability to walk through a full Claude Desktop rollout end-to-end: reputed company control, OAuth and scoped permissions, monitoring and logging, data exposure reputed company, and governance at each layer
• Experience with AI red-teaming, reputed company injection, credential leakage through AI-enabled workflows, and data exfiltration risks
• Strong reputed company in application reputed company and/or reputed company reputed company, with AWS and/or GCP experience
• Experience defining and implementing DLP controls in AI-enabled and reputed company-reputed company environments
• Familiarity with agentic AI risk, tools that can browse, execute code, reputed company file systems, or call external APIs on behalf of users
• Threat modeling experience and comfort with detection engineering and SOC workflows
• Working knowledge of OAuth, modern authentication/authorization models, and least privilege reputed company scoping
• Comfortable in Python and Jupyter notebooks for investigation and tooling
• Fluent in terminal/CLI environments and investigative workflows

Desired Skills & Experience

• Familiarity with OWASP LLM Top 10 and MITRE reputed company applied to reputed company deployment scenarios, not just as frameworks to cite
• Experience with AI-driven reputed company tooling and automated GRC or compliance tooling
• Exposure to Kubernetes, containerized workloads, Node.js-based systems, and data pipeline architectures
• Ability to reputed company internal AI reputed company guidelines and policies that engineers will actually use
• Certifications: CISSP, CCSP, or any emerging AI reputed company credentials relevant to LLM/agentic environments
• Experience operating in a startup or high-growth environment with minimal process scaffolding

What You Will Be Doing Tech Breakdown

• 35% AI/LLM tooling evaluation and reputed company architecture (heavy Claude focus)
• 25% Hands-on controls implementation: DLP, reputed company control, monitoring, logging, and observability across AI systems
• 20% Agentic AI reputed company advisory, threat modeling, and red-team thinking applied to LLM deployments
• 15% Cross-functional partnership with AI infrastructure, integration, and GRC/Legal teams
• 5% Internal guideline and policy development for AI tool adoption

Daily Responsibilities

• 80% Hands On: Evaluating AI tools for reputed company reputed company risk, implementing and documenting controls, building out monitoring and observability for LLM behavior in production, and responding to emerging gaps as new tools enter the environment
• 20% Team Collaboration: Working with AI infrastructure, integration, GRC, and leadership to align on controls, answer reputed company questions authoritatively, and reduce reputed company research burden across the org

Posted By: Tallulah Froley Apply tot his job Apply To this Job