Security Engineer - LLM SecOps

Key Responsibilities

Design and implement guardrail systems for securing LLM inputs and outputs in production environments

Secure Retrieval-Augmented Generation (RAG) pipelines, vector databases, and embedding workflows

Develop runtime monitoring solutions for AI systems, including prompt logging, context analysis, and response validation

Secure LLM API endpoints through authentication, rate limiting, and abuse prevention mechanisms

Detect and respond to AI-related security incidents using structured Root Cause Analysis (RCA) methodologies

Manage AI supply chain risks, including third-party models, APIs, and plugin integrations

Collaborate closely with Product Security, Machine Learning Engineering, and Platform teams.

Required Qualifications

LLM Security

Experience with prompt injection defense, jailbreak detection, and output filtering

Familiarity with OWASP Top 10 for LLM Applications

Data & RAG Security

Knowledge of vector database security and embedding pipeline protection

Experience with data isolation in multi-tenant environments and PII protection

Monitoring & Incident Response

Experience with logging, anomaly detection, and SIEM integration for AI systems

Strong understanding of incident response frameworks and RCA practices

Cloud & Infrastructure

Hands-on experience with AWS environments

Knowledge of Kubernetes security, container isolation, and IAM/RBAC models

Experience securing APIs using OAuth, JWT, and rate limiting techniques

Engineering

Strong proficiency in Python for automation, scripting, and security tooling

Preferred Qualifications

• Experience in fintech, payments, or regulated financial services environments

• Exposure to AI security platforms such as Lakera, Protect AI, HiddenLayer, or similar tools

• Relevant certifications (e.g., AWS Security Specialty, CISSP, OSCP)

• Familiarity with regulatory and data protection frameworks (e.g., PCI-DSS, FCRA)

• Technology Environment

• Cloud: AWS (EKS, Aurora MySQL)

• Data Processing: Apache Spark

• Backend: Python, Kotlin

• Frontend: JavaScript, TypeScript, React / React Native

• Security & Operations: mTLS, CDN/Edge Security, SLI/SLO frameworks, RCA practices