Principal Engineer, AI, Safe Security

• 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
• If you’re passionate about cyber risk, thrive in a fast-paced environment, and want to be part of a team that’s redefining security—we want to hear from you!

Desirable

• 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
• 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