Principal Engineer - AI

Job description

Most boards and executives are currently flying blind when it comes to cyber risk. They are guessing. At Safe, we’ve built an AI-driven engine that finally gives the C-Suite a clear, quantified, and real-time view of their security posture. We don’t just provide data; we provide **certainty**. We are a **$170M Series C-funded** category leader. We don’t play in the mid-market; we operate at the highest levels of global enterprise. Today, we are proud to **serve 10% of the Fortune 500**, protecting global icons such as **Apple, Netflix, AT&T, Verizon, and Victoria’s Secret**. As we scale toward our next chapter, we are looking for high-performers who want to do the best work of their careers at the intersection of AI and Cybersecurity. ### [**The Culture Memo: Our Operating System**](https://jobs.safe.security/culture/) Safe is not a typical corporate environment. We are a high-intensity, mission-driven team. We value builders who want to define a category and work alongside people who are equally committed to excellence. - **Extreme Ownership:** We don’t do "not my job." We hire people who see a gap and own the solution from start to finish. - **The Elite Standard:** We serve the most sophisticated companies on the planet. Our work must be bulletproof. Whether it’s a line of code or a sales deck, we aim for Tier-1 quality every time. - **Methodology & Rigor:** We don’t wing it. From **Force Management** and **MEDDICC** in sales to **data-driven sprints** in engineering, we rely on proven frameworks to stay disciplined and predictable. - **Radical Candor:** We move too fast for politics or sugar-coating. We value direct, honest feedback that helps us find the right answer quickly. - **The Series C Hustle:** We have the stability of a well-funded leader but the heart of a startup. ### **The Perks & Ownership:** We want our team to feel like owners because they **are** owners. We trust our people to manage their results and their time. - **Meaningful Equity:** Every "Safestar" is a shareholder. You aren’t just an employee; you are a partner in our success. - **Unlimited Leaves:** We don’t believe in clock-watching. We offer **unlimited leave** because we trust you to take the time you need to recharge while staying committed to the mission. - **Comprehensive Benefits:** We provide top-tier medical insurance and wellness benefits to ensure you and your family are well cared for. - **Career Trajectory:** We are growing aggressively. For high-performers, the path for advancement moves at the speed of your ambition. As a Principal Engineer - AI, you will define and lead the technical direction of AI systems that power Safe’s CRQ, CTEM, and TPRM products, including agentic workflows, RAG pipelines, LLM orchestration, and AI-native developer tooling. You’ll be the hands-on architect behind Safe’s AI engineering stack, bridging model intelligence with production-grade infrastructure. You’ll collaborate with product, data, and platform teams to design scalable, explainable, and enterprise-ready systems. This is a high-impact, technical leadership role that will shape how AI is built, deployed, and governed across Safe. ### Core Responsibilities: - **Architect Safe’s AI Systems:** Design and scale AI-driven components — LLM orchestration, retrieval-augmented generation (RAG), vector stores, prompt pipelines, and AI microservices. Drive architecture for AI observability, safety, and evaluation (precision, recall, F1, hallucination detection, cost metrics). - **Productionize AI Agents:** Build multi-turn, goal-oriented agent systems that automate reasoning across TPRM, CTEM, and CRQ domains (e.g., control reviews, issue RCA, automated responses). Ensure reliability, traceability, and deterministic behavior in production. - **AI Infrastructure & Platform Ownership:** Partner with Platform & DevOps teams to operationalize model serving (AWS SageMaker, Bedrock, or self-hosted Llama), build AI APIs, and manage model lifecycle and versioning. Establish feature stores, embedding management, and in-memory retrieval layers. - **Data Pipeline & Knowledge Graph Integration:** Work with Data Engineering to design pipelines for structured and unstructured data ingestion, semantic indexing, and context retrieval (Snowflake + Iceberg + LlamaIndex). - **AI Evaluation, Monitoring & Governance:** Define internal frameworks for golden dataset validation, LLM evaluation (LangFuse/LangSmith), and safety enforcement policies. Implement human-in-the-loop (HITL) mechanisms and continuous feedback loops. - **Mentor & Multiply:** Guide AI and backend engineers on architectural design, experimentation methodologies, and prompt optimization. Collaborate with product leaders to translate abstract AI goals into measurable engineering deliverables. ### Minimum Qualifications: - Experience: 12+ years total experience in software engineering, including 4+ years building AI/ML systems or large-scale data/LLM infrastructure. - Core Technical Skills: - Strong programming fundamentals in Python, Go, or TypeScript - Deep understanding of LLM-based architectures, prompt engineering, and RAG pipelines - Hands-on experience with LangChain, LlamaIndex, or equivalent orchestration frameworks - Vector databases (FAISS, Pinecone, Weaviate, Redis Vector, or Milvus) - Cloud model deployment (AWS SageMaker, Bedrock, Vertex AI, or custom inference APIs) - Data systems: Snowflake, Iceberg, S3, Postgres/MySQL - MLOps & Infra: Familiar with model versioning, CI/CD for ML, and performance optimization for real-time inference. - Applied AI Focus: Practical understanding of evaluation metrics, hallucination detection, RAG reliability, and enterprise AI safety. ### Preferred Qualifications: - Experience integrating AI into cybersecurity or risk management products - Familiarity with multi-agent systems and autonomous workflows (CrewAI, LangGraph, AutoGen) - Experience building AI evaluation dashboards and AI observability stacks - Knowledge of knowledge graphs, semantic search, or retrieval pipelines - Exposure to data governance, compliance, or SOC2/ISO 27001 environments - Published research, open-source contributions, or prior leadership of AI teams is a strong plus