Principal Engineer - AI
Software Engineering, Data Science
Bengaluru, Karnataka, India
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
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.
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.
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