Chief AI Security Architect

24 hours ago

Bengaluru, Karnataka, India beBeeSecurity Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Title:

Chief AI Security Architect

This role involves designing and implementing advanced security measures to protect against potential threats. The ideal candidate will have experience in architecting and deploying secure AI systems, as well as a deep understanding of machine learning and data privacy.

Required Skills and Qualifications:

  • AI/ML Expertise: Proven track record designing, developing, and deploying production-quality AI/ML solutions-especially generative AI systems-spanning data ingestion, prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), and inference optimization.
  • Large Language Model Proficiency: Hands-on experience with foundation models (e.g., GPT, PaLM, LLaMA) and open-source alternatives; adept at prompt design, chain-of-thought engineering, embedding creation, and custom fine-tuning workflows.
  • GenAI Infrastructure and MLOps: Familiarity with MLOps pipelines for GenAI (model versioning, CI/CD, monitoring, A/B testing), container orchestration (Docker/Kubernetes), and scalable deployment of LLMs in cloud or on-prem environments.
  • Data and Vector Store Management: Ability to build and maintain scalable data pipelines and vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient semantic search and knowledge retrieval.
  • Robust Software Engineering: Strong coding skills in Python (and/or Java, Go) with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn) and building secure, maintainable APIs and microservices.

Benefits:

  • Opportunity to work on cutting-edge AI security projects
  • Collaborative team environment
  • Professional growth and development opportunities

Others:

  • Define and drive AI security/privacy solutions: Architect and implement end-to-end GenAI and ML systems-agents, RAG pipelines, fine-tuned LLMs, and custom models-that proactively detect and mitigate security threats and privacy risks.
  • Build and operate platforms: Develop scalable data pipelines, vector stores, and MLOps infrastructure (CI/CD, monitoring, model-versioning) to support continuous delivery of AI-driven security and privacy features.
  • Integrate with ecosystem: Embed AI capabilities into user-facing products, downstream services, and core infrastructure tools-designing APIs, microservices, and tooling for seamless adoption by engineering teams.
  • Cross-functional collaboration: Partner closely with Product, Legal, Policy, and Security Operations to translate requirements into technical specs, ensure compliance with data-privacy regulations, and align on risk-mitigation strategies.

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