Headline

AI Security Engineering | LLM Red Teaming | Adversarial ML | Building AI-Driven Security Automation

About

Working at the intersection of Adversarial Machine Learning, LLM Security, and Offensive Security Automation, with 1.8 years of hands-on experience building and securing AI systems. Skilled in PyTorch, LangChain, LangGraph, RAG architectures, and multi-agent systems, with practical exposure to FGSM, PGD, BadNets, OWASP LLM Top 10, and MLSecOps practices. Focused on developing AI-native security solutions that improve system robustness, reduce manual effort, and accelerate vulnerability detection and remediation. Currently a Scholar at SAP Labs, contributing across AI/ML engineering, offensive security testing, and GenAI platform development on SAP AI Core and BTP. Experience includes working on AI security research, red teaming workflows, and automation-driven pentesting systems, with an emphasis on building scalable and secure AI-driven applications. Looking ahead, aiming to specialize in AI Security Architecture, LLM Red Teaming, and MLSecOps at scale, with a focus on securing production-grade GenAI systems. Interested in building research-driven, real-world security solutions and contributing to the development of trustworthy and resilient AI systems.

Location

Bengaluru

AI stack

cursor

opencode

claudecode

Work experience

Scholar@SAP | AI Security • ML Systems • GenAI at SAP, Aug 2024 - Present · 2 yrs 1 mo, Bengaluru, 🔹 GenAI Development — (Jan 2026 – Present) - Built RAG-driven pipelines for ABAP code analysis and S/4HANA migration automation, generating Clean Core recommendations and structured code-fix proposals from ATC findings using knowledge retrieval, similarity validation, and metadata-enriched LLM outputs - Continuing to independently build and ship AI security tooling - including adversarial ML systems, agentic penetration testing frameworks, and LLM red teaming pipelines - as active side projects alongside current role 🔹 AI Security Engineering — (May – Dec 2025) - Designed and deployed a suite of AI-native offensive security systems - spanning adversarial ML research, multi-agent penetration testing, vulnerability triage, and autonomous payload generation - end-to-end across the full offensive security lifecycle - Replaced manual pentesting and revalidation workflows with agentic automation pipelines, combining static request analysis, dynamic execution, and context-aware retrieval - enabling real-time traffic classification, intelligent payload mutation, and scalable vulnerability validation across integrated security tooling ecosystems 🔹 Applied AI/ML Development — (Sep 2024 – Mar 2025) Engineered production-scale ML pipelines for document validation and duplicate detection, leveraging multi-modal inference (transformer-based NLP + contrastive CV embeddings) with vector similarity search to deliver precision/recall-optimized performance at scale

Internship Trainee at INFONSL SECUNET PVT. LTD., Aug 2023 - Jun 2024 · 11 mos, Ranchi

Education

Master of Technology - MTech, BITS Pilani Work Integrated Learning Programmes

Bachelor's degree, Information Technology, st. xavier's college, ranchi