Salary
≈ $19k – $48k per year (Estimated)
Location
In office (Bengaluru)
Overview
Company
Impact
Profile match
Wayfair is the online destination for the home and a pure-play online retailer that offers a wide range of home goods and furniture products for the bedroom, living room, kitchen and dining, bathroom, and more.
Responsibilities:
- Assist in building and deploying security solutions that will be used across the enterprise.
- Liaise with development and product teams to develop secure products and features for customers, suppliers, partners, and employees.
- Implement Sec in a DevSecOps model of operations.
- Perform penetration testing and code reviews of highly complex services that are used by millions of customers, leveraging AI-assisted tooling to expand coverage and accelerate findings.
- Conduct risk analysis and threat modelling, leveraging AI to accelerate analysis and broaden coverage.
- Build threat models for AI/LLM-powered features (prompt injection, model abuse, sensitive data exposure through RAG and agents).
- Assist in onboarding applications to web application firewall (WAF), maintain and tune WAF rules.
- Triage Bug Bounty reports and coordinate with external hackers as part of Wayfair's Bug Bounty Program.
- Build automations to streamline security workflows and detections, including LLM-powered tooling for triage, code review, report summarisation, and vulnerability discovery.
- Use AI coding assistants (e. g., Claude, Cursor, Copilot) as a daily multiplier - for writing detection logic, drafting remediation guidance, scripting one-off analysis, and for code reviews.
Requirements:
- 0-1 year of experience in the cybersecurity domain.
- Hands-on scripting experience in Python.
- Exposure to k8s and cloud platforms (Google Cloud preferred).
- Exposure to securing applications, REST and GraphQL services.
- Understanding of secure application development or Secure SDLC.
- Understanding of build and release management, CI/CD platforms.
- Exposure to dynamic analysis.
- Understanding of Authentication mechanisms such as JWT and OAuth.
- Experience with utilising AI assistants (Claude, Cursor, Copilot, or similar) for day-to-day work.
What we'd love to see (but isn't required):
- Experience securing any of these cloud service platforms - GCP, AWS, Azure.
- Comfort using AI coding assistants (Claude, Cursor, Copilot, or similar) as part of day-to-day security work.
- Experience building security automation with LLMs - e. g., bug bounty triage, code review assistants, alert enrichment, or report normalisation.
- Hands-on experience assessing the security of AI/LLM-powered applications - prompt injection, jailbreaks, insecure tool/agent use, RAG data exposure, model abuse.
- Familiarity with AI security frameworks such as OWASP LLM Top 10 MITRE ATLAS, or NIST AI RMF.
- Relevant security certifications like GCP Professional Cloud Security Engineer, OSCP, OSWE, CKS, GWAPT, GPEN, GSEC.
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