Principal Engineer - AI

Safe Security; Bengaluru; On-site; INR 2,500,000 - 3,500,000; Full time

Benefits offered by this job

Meaningful Equity

Unlimited Leaves

Comprehensive Benefits

Career Trajectory

Job summary

Safe Security in Bengaluru is on the lookout for a Principal Engineer - AI who will lead the design and development of AI-driven systems for their cutting-edge cybersecurity products.

The ideal candidate has over 12 years of software engineering experience with a focus on AI/ML systems. The position emphasizes innovation, scalability, and direct impact within the company.

Safe Security offers competitive benefits, meaningful equity, and opportunities for growth in a high-performance culture.

Qualifications

• 12+ years in software engineering, with 4+ years in AI/ML systems.
• Strong programming fundamentals and experience with LLM architectures.
• Familiarity with CI/CD for ML and performance optimization.

Responsibilities

• Architect and design scalable AI systems for Safe's products.
• Build production-grade multi-turn agent systems for automation.
• Collaborate with teams to integrate AI into development workflows.

Skills

Python

Go

TypeScript

LangChain

RAG pipelines

Data systems

MLOps

Tools

AWS SageMaker

Snowflake

Postgres/MySQL

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

• 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

• 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

We want to hear from you!