AI Agent Security & Governance Engineer

Role: AI Agent Security and Governance Engineer

Experience:

Location:

Work Mode:

Domain:

Join Time:

Employment Type:

Role Summary:

AI Agent Security & Governance Engineer

Key Responsibilities:

• AI/LLM Security Engineering

• Secure AI/ML pipelines, LLM APIs, RAG systems, vector databases, and agentic AI workflows.
• Implement controls against

  prompt injection, adversarial ML attacks, data poisoning, model inversion, model theft

  , and harmful agent actions.
• Embed security-by-design into AI development and deployment lifecycles.

• Governance, Compliance & Risk Management

• Perform AI-focused

  threat modeling

  , bias risk assessment, and security posture evaluation. Develop processes for safe, explainable, auditable, and ethical AI usage
• Define AI governance controls for Healthcare/Life Sciences, ensuring compliance with

  HIPAA, GDPR, SOC2

  , and internal policies.

• Security Operations & Monitoring

• Monitor AI agents and ML models for drift, anomalies, misuse, hallucinations, and adversarial behavior.Investigate and resolve incidents involving AI security breaches or misbehavior.
• Build automated pipelines for red-teaming, adversarial testing, and model robustness validation.

• Collaboration & Cross-Functional Enablement

• Work closely with data scientists, ML engineers, DevSecOps, product owners, and clinical domain teams.
• Develop AI security documentation, runbooks, and governance playbooks.
• Lead training sessions for engineering teams on AI/ML security best practices.

• Required Skills & Experience

• 6–12 years

  of experience across Cybersecurity, AI/ML Security, Application Security, or Cloud Security.

• Cybersecurity expert

  with proven hands-on experience implementing security protocols to safeguard AI systems, models, and data workflows.

• Deep understanding of cyber security frameworks, methodologies, and industry standards

  , including NIST, MITRE ATT&CK, OWASP, ISO27001.
• Experience with

  LangChain, LangGraph, Guardrails AI, Bedrock/Gemini/OpenAI

  integrations.
• Exposure to privacy technologies such as differential privacy, tokenization, and federated learning.

• Track record of staying current

  with new AI threats, emerging vulnerabilities, and evolving security best practices.
• Experience securing cloud environments (

  AWS / GCP / Azure

  ) and containerized systems (Kubernetes, Docker).
• Strong technical knowledge of LLM architecture, embeddings, RAG mechanisms, vector stores, and agentic AI frameworks.
• Familiarity with adversarial ML methods: prompt injection, model inversion, membership inference, data poisoning.
• Proficiency in Python or similar languages for automation and testing.
• Understanding of

  Healthcare/Life Sciences security and compliance

  (HIPAA, PHI, data sensitivity).

📩 Apply Now!

Send your updated resume to careers@sidinformation.com