AI Security & Platform Engineer in Bangalore, भारत

Responsibilities

• Adversarially test AI agents and workflows for prompt injection, context poisoning, unsafe tool use, data leakage, unauthorized access, and unintended behavior.
• Threat-model AI features, agent workflows, model integrations, tool calling, customer-facing applications, and security-sensitive architecture changes.
• Build automated adversarial evaluations, security regression tests, test harnesses, monitoring, and reusable platform guardrails.
• Implement least-privilege access, scoped credentials, sandboxing, runtime policy enforcement, human approval, output validation, containment, and kill switches.
• Review and secure applications, APIs, file uploads, webhooks, exports, third-party integrations, authentication, authorization, tenant isolation, and data handling.
• Harden cloud permissions, service identities, secrets, storage, networking, deployment pipelines, and production access.
• Improve model-provider routing, retries, timeouts, fallbacks, quotas, rate limits, cost controls, and observability.
• Reproduce vulnerabilities, assess real-world impact, implement production-ready fixes, support debugging, and investigate application, infrastructure, and AI-security incidents.

Requirements

• 2–3 years of hands-on experience across AI engineering, backend or platform engineering, application security, infrastructure security, DevSecOps, or a related engineering role.
• Experience building, operating, or securing production AI systems using LLM APIs, agents, tool calling, RAG, model gateways, or similar technologies.
• Strong programming ability in Python, TypeScript, Go, or another relevant language.
• Understanding of authentication, authorization, injection, tenant isolation, secrets, unsafe data handling, and common web and API security risks.
• Practical experience with cloud infrastructure, CI/CD, containers, monitoring, or infrastructure-as-code.
• Ability to investigate, reproduce, assess, and remediate suspected vulnerabilities using source code, logs, traces, metrics, and database queries.
• Ability to reason about trust boundaries among users, models, customer data, application code, tools, and third-party services.
• Hands-on AI red teaming, adversarial model testing, prompt-injection defense, agent sandboxing, multi-tenant SaaS security, security test harnesses, fuzzers, SAST, DAST, dependency scanning, secret scanning, infrastructure scanning, model gateways, AI observability, LLM evaluations, bug bounties, CTFs, security research, or relevant open-source experience are preferred.
• Familiarity with OAuth, webhooks, file processing, MCP, third-party integrations, SOC 2, or ISO 27001 from an engineering implementation perspective is preferred.