7AI empowers Security teams to shift high-value tasks to intelligent AI agents that help reshape the future of cybersecurity and automation. We’re building at the bleeding edge of AI, blending deep engineering with practical product impact. You’ll collaborate with mission-driven teams in a fast-paced, high-growth environment where your contributions directly influence what’s next in AI-powered systems.
This role builds on, but is distinct from, traditional ML engineering: the focus is not on training models from scratch, but on composing, optimizing, and scaling AI systems that solve complex enterprise problems.
What You’ll Do
Architect and build LLM-powered systems — design retrieval workflows, context management, agent prompts, and structured output pipelines.
Orchestrate AI workflows using tools like LangChain, LlamaIndex, or similar frameworks, integrating them with product APIs and backend services.
Drive prompt engineering and iteration — refine prompts, templates, and context strategies to meet product quality and reliability goals.
Manage real-world evaluation metrics — measure usefulness, factual correctness, latency, and UX impact vs. classic accuracy alone.
Collaborate across functions — work closely with product, platform, and backend teams to ensure seamless integration.
Develop reliable, scalable deployments — focus on performance, cost efficiency, and observability in production environments.
Who You Are
Experienced in building real LLM applications — you’ve shipped systems that use large models meaningfully.
Strong software engineering skills — Python/TypeScript, API design, backend integration, and cloud deployment.
Tool fluency — comfortable with RAG, vector databases (e.g., Pinecone/Weaviate), workflow frameworks (LangChain, Dust), and related tooling.
Architectural thinker — you can diagram end-to-end solutions incorporating context windows, caching strategies, tool calls, and multi-step reasoning.
Product-oriented — you care not just that the AI works, but that it delivers value safely and reliably to users.
Basic Qualifications
6+ years of software engineering experience, with at least 1+ year working building AI in production.
BS Degree in Computer Science or related field. Masters degree is a plus.
Demonstrated use of LLMs in production workflows or complex prototypes.
Strong coding ability in Python or equivalent; familiarity with backend frameworks and cloud services.
Experience with API integrations, database systems, and scalable architectures.
Experience with multi-modal models or multi-agent system design.
Familiarity with AI safety guardrails, hallucination mitigation, and structured output enforcement.
Knowledge of vector DBs, RAG architectures, and prompt lifecycle tooling.
