Senior AI Engineer

About 7AI 7AI is the foundational AI security company. Founded in 2024 by Cybereason co-founders Lior Div and Yonatan Striem-Amit, we came out of stealth in February 2025 to take on the non-human work of the SOC. Our platform pairs a federated SIEM with AI agents that detect, investigate, respond, and hunt, plus dedicated threat hunting and threat intelligence, with humans on the loop. Backed by $166 million in total funding from investors including Index Ventures and Blackstone, we're building the AI foundation for security operations in the agentic era, and we're still early. That's the appeal: you'll join at a pivotal stage where your work shapes what 7AI becomes, alongside security veterans who've built this before. Our culture runs on respect, collaboration, and a shared bar for excellence, all pointed at one goal, delivering real outcomes for the customers who trust us to defend them. Overview Every AI agent's investigation is only as good as the LLM system underneath it. Get the retrieval, context, and prompting wrong, and the agent looks smart in a demo but falls apart on a real customer's data. As a Senior AI Engineer, you'll build the LLM-powered systems behind our security agents: retrieval workflows, context management, agent prompts, and structured output pipelines. This role is distinct from traditional ML engineering. Instead of training models from scratch, [... source excerpt omitted ...] you'll compose, optimize, and scale AI systems that solve complex enterprise problems, working closely with product, platform, and backend teams to get them into production. What You'll Do Architect and build LLM-powered systems, including retrieval workflows, context management, agent prompts, and structured output pipelines. Orchestrate AI workflows using LangChain, LlamaIndex, or similar frameworks, and integrate 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 such as usefulness, factual correctness, la [... source excerpt omitted ...] product, platform, and backend teams to ensure integrations land cleanly. Build reliable, scalable deployments that hold up on performance, cost efficiency, and observability in production. What We're Looking For 6+ years of software engineering experience, including at least 1 year building AI in production. BS in Computer Science or a related field. Shipped LLM applications in production, not just prototypes, using large models in ways that meaningfully mattered to 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,