The Role
Lead the technical direction of AI systems at SAFE Security, architect scalable AI components, productionize AI agents, and mentor engineers.
Summary Generated by Built In
At SAFE Security, our mission is bold and ambitious: We Will Build CyberAGI — a super-specialized system of intelligence that autonomously predicts, detects, and remediates threats. This isn’t just a vision—it’s the future we’re building every day, with the best minds in AI, cybersecurity, and risk. At SAFE, we empower individuals and teams with the freedom and responsibility to align their goals, ensuring we all move towards this goal together.
We operate with radical transparency, autonomy, and accountability—there’s no room for brilliant jerks. We embrace a culture-first approach, offering an unlimited vacation policy, a high-trust work environment, and a commitment to continuous learning. For us, Culture is Our Strategy—check out our Culture Memo to dive deeper into what makes SAFE unique.
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:
- 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.
- 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
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
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! 🚀
Top Skills
Aws Sagemaker
Bedrock
Faiss
Go
Iceberg
Llamaindex
Milvus
Pinecone
Python
Redis Vector
Snowflake
Typescript
Weaviate
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The Company
What We Do
Safe Security is a pioneer in the “Cybersecurity and Digital Business Risk Quantification” (CRQ) space. It helps organizations measure and mitigate enterprise-wide cyber risk in real-time using it’s ML Enabled API-First SAFE Platform by aggregating automated signals across people, process and technology, both for 1st & 3rd Party to dynamically predict the breach likelihood (SAFE Score) & $$ Value at Risk of an organization
Headquartered in Palo Alto, Safe Security has over 200 customers worldwide including multiple Fortune 500 companies averaging an NPS of 73 in 2020.
Backed by John Chambers and senior executives from Softbank, Sequoia, PayPal, SAP, and McKinsey & Co., it was also one of the Top Contributors to the National Vulnerability Database(NVD) of the U.S. Government in 2019 and the ATT&CK MITRE Contributor in 2020.
The company, since 2018, has also been working with MIT in joint research for the development of their SAFE Scoring Algorithm. Safe Security has received several awards including the Morgan Stanley CTO Innovation Award.
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