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7AI is a foundational AI security company founded in 2024 by Cybereason co-founders, backed by $166 million in funding from Index Ventures and Blackstone. The company pairs a federated SIEM with AI agents that detect, investigate, respond, and hunt threats, plus dedicated threat hunting and threat intelligence capabilities.
As a Senior AI Engineer, you will build the LLM-powered systems that power 7AI's security agents. This role focuses on composing, optimizing, and scaling AI systems to solve complex enterprise security problems—not training models from scratch, but architecting production-grade LLM applications.
Key responsibilities include:
- Architect and build LLM-powered systems: retrieval workflows, context management, agent prompts, and structured output pipelines
- Orchestrate AI workflows using frameworks like LangChain, LlamaIndex, or similar tools, integrating them with product APIs and backend services
- Own prompt engineering and iteration, refining prompts, templates, and context strategies to meet product quality and reliability goals
- Track real-world evaluation metrics (usefulness, factual correctness, latency, user experience impact) rather than just classic accuracy metrics
- Collaborate closely with product, platform, and backend teams to ensure clean integrations
- Build reliable, scalable deployments that perform well on latency, cost efficiency, and observability in production
You'll work alongside security veterans in a culture emphasizing respect, collaboration, and excellence, joining at a pivotal stage where your work shapes the company's direction.
REQUIREMENTS:
- 6+ years of software engineering experience, including at least 1 year building AI in production
- BS in Computer Science or related field
- Shipped LLM applications in production (not prototypes) using large models in ways that meaningfully impacted the product
- Strong coding skills in Python or equivalent, with experience in API design, backend integration, database systems, and cloud deployment
- Hands-on experience with RAG, vector databases (Pinecone, Weaviate), and workflow frameworks like LangChain or Dust
- Comfortable architecting end-to-end solutions including context windows, caching strategies, tool calls, and multi-step reasoning
- Experience with multi-modal models or multi-agent system design
- Familiar with AI safety guardrails, hallucination mitigation, and structured output enforcement
- Focused on product outcomes: AI must work safely and reliably for users, not just perform well on paper
NICE TO HAVE:
- Master's degree in Computer Science or related field
