Senior AI Security Engineer

About the role

The company is looking for a software engineer first, with hands-on ML/LLM experience from a security perspective. You take findings from penetration tests and AI red-team exercises, find the root cause in code, models, pipelines or cloud configuration, and ship durable fixes. Beyond fixing, you build defences such as guardrails around model endpoints, secure patterns for RAG and agents, and security checks in CI/CD, working on your own AI products and supporting client engagements where remediation is in scope.

Key responsibilities

• Triage penetration test and AI red-team findings, reproduce them and identify root cause, then ship fixes in application code, APIs, model-serving layers, data pipelines and cloud infrastructure.
• Add regression tests for every fixed issue, including adversarial prompt tests in CI, and track findings to closure while reporting status to engineering leads and clients.
• Design and deploy guardrails around model endpoints: input and output filtering, prompt-injection detection, PII redaction and policy enforcement.
• Build secure patterns for RAG and agents: least-privilege tool access, sandboxed execution, human approval for risky actions and tenant isolation.
• Secure the ML supply chain: model weights, datasets and dependencies (signing, scanning, provenance).
• Add logging, monitoring and alerting for AI workloads and write incident runbooks for AI-specific events.
• Threat-model new AI features against the OWASP Top 10 for LLM Applications, MITRE ATLAS and NIST AI RMF.
• Build security checks into CI/CD (SAST, dependency and secret scanning, model scanning) and review code and architecture for AI products and vendor AI services.
• Train developers on secure AI patterns and publish reusable libraries and templates.

Required profile

• 5-7 years in software engineering, application security or security engineering, including at least 2 years in a security-focused role.
• Hands-on work securing or building at least one LLM or ML application in production.
• Bachelor's or Master's degree in Computer Science, Software Engineering, Cybersecurity, Artificial Intelligence or a related discipline, or equivalent practical experience.

Required skills

• LLM and GenAI engineering: LLM APIs (OpenAI, Anthropic, Azure OpenAI, Vertex AI), RAG, embeddings, vector databases, agent frameworks such as LangChain or LlamaIndex.
• AI threat mitigation mapped to OWASP Top 10 for LLM Applications and MITRE ATLAS.
• Guardrails and secure-by-design AI, using tools such as NeMo Guardrails, Llama Guard, Lakera or Presidio.
• Application security fundamentals: OWASP Top 10, authentication and authorisation, secrets management, cryptography basics.
• ML fundamentals and supply chain security, including ModelScan.
• DevSecOps and CI/CD: SAST (Semgrep), Snyk, Dependabot, Gitleaks, Garak, PyRIT, Promptfoo.
• Containers and cloud security: Docker, Kubernetes, IAM, networking, and AI workloads on AWS, Azure or GCP.