LLM Agent Engineer and DevSecOps
Opportunity Summary
This senior role supports an AI-native oncology platform that orchestrates multimodal data and large language model workflows. You may contribute to AI agent and copilots for business and scientific teams, including building MCP-style servers and pipelines into a governed knowledge graph. Alternatively, you may own DevSecOps for agent infrastructure, including threat modeling, agent permissioning, secure CI/CD, and incident response aligned to regulated data governance. A strong candidate has shipped LLM workflows in production, with expertise in Python and TypeScript, or deep security engineering experience on GCP with infrastructure as code.
Ideal Candidate
A senior engineer who either ships LLM workflows with production rigor or builds secure, observable infrastructure for AI systems. Comfortable partnering cross-functionally and taking ownership of complex systems.
Project Insights
- Governed knowledge graph suggests strong attention to data lineage
- MCP-style servers implies tool and interface integration work
- Small expert teams mean your ownership will be highly visible
How to Prepare
- For agent work, prepare examples of LLM workflow evaluation and monitoring
- For security work, bring incident response stories and security-by-design approaches
- Expect to discuss compliance-aligned governance and least-privilege access patterns
Typical Challenges
- Keeping LLM agent outputs safe and consistent under real usage
- Implementing secure agent permissioning and access controls
- Setting up monitoring and incident response for AI agent risks
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Project Details
Skills & Requirements
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