[EJV] Lead AI Security Engineer (LLM & Agentic AI) - with Joining Bonus

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About the role

🎁 Q4 JOINING BONUS Join BGSV by  31 December 2026  and receive a Joining Bonus equivalent to  1 month gross salary , subject to program eligibility and terms & conditions. About the Role We are looking for a Lead AI Security Engineer  to own safety evaluation and security testing across Bosch's growing portfolio of AI-enabled products and internal tooling — large language model deployments and agentic AI systems (including Model Context Protocol – style tool-calling integrations), and the enterprise services they connect to. You will lead both sides of the same coin: whether an AI system behaves safely (does it refuse harmful requests, avoid unsafe recommendations, stay within its intended scope) and whether it is secure (can it be manipulated via prompt injection, tricked into misusing its tools, or made to leak data it shouldn't). This is a cross-business-unit mandate, not a single product line — you will build the practice largely from the ground up. Key Responsibilities • Design and run safety evaluations for LLM-based features and AI agents: red-teaming for harmful, biased, or unsafe outputs; jailbreak and guardrail-bypass testing; systematic benchmarking against known safety failure modes. • Design and run security testing for the same systems: prompt injection (direct and indirect), excessive agency, tool/plugin abuse, and confused-deputy risks in agentic and MCP-style tool-calling architectures. • Build and maintain a threat model covering the full path from user input through model reasoning to tool execution — identifying where a safety failure and a security failure are really the same underlying gap. • Verify human-in-the-loop and guardrail controls are architecturally sound wherever an AI agent can take a consequential action (a write to a business system, an automated communication, a code change) — not just present in a UI that can be talked around. • Scope, brief, and review the output of external red-teaming/pentest vendors; write test specifications and acceptance criteria for AI-specific engagements. • Track and apply emerging frameworks — OWASP Top 10 for LLM Applications, OWASP Top 10 for Agentic Applications, the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act — translating new research into concrete internal test requirements before it becomes an incident. • Partner with AI/ML engineering teams across business units to get safety and security reviewed into system design, not only tested after the fact. • Produce evidence and documentation supporting AI governance and compliance needs (ISO/IEC 42001 AI management system, EU AI Act risk assessments, internal responsible-AI review processes). • Build test tooling as needed: adversarial-prompt harnesses, automated jailbreak/red-team pipelines, and MCP/tool-schema fuzzers. • Mentor engineers on safe and secure AI system design, and grow the practice as it scales across more teams and products. Required Qualifications • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. • 6+ years in security testing • 2+ years of experience in AI/ML safety research or a closely related discipline, with a track record of finding real issues in real systems. • 2+ years of practical experience testing or red-teaming LLM/agentic AI systems — prompt injection, jailbreaks, excessive agency, tool/plugin abuse — independent research or open-source contributions are acceptable given how new this specialty is. • Working knowledge of at least one AI safety/governance framework (NIST AI RMF, ISO/IEC 42001, EU AI Act) and how it translates into concrete engineering requirements. • Enough coding ability (Python and/or JavaScript/Node) to build evaluation harnesses and test tooling rather than rely solely on manual tes

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