Innovative AI/ML Cloud Security Leader

1 day ago

Bengaluru, Karnataka, India beBeeCloud Full time ₹ 1,59,00,000 - ₹ 2,31,50,000
AI/ML Cloud Security Lead

As a seasoned leader in AI/ML and cloud security, you will drive initiatives that combine leadership, collaboration, and technical expertise to design innovative AI-driven solutions, manage high-impact projects, and guide teams in solving complex security challenges in cloud environments.


Key Responsibilities:
  • Lead cross-functional teams in the development of AI/ML models for threat detection, anomaly detection, and predictive analytics in cloud and network security.
  • Oversee the architecture and implementation of scalable data pipelines for processing large-scale datasets from logs, network traffic, and cloud environments.
  • Manage the application of MLOps best practices to ensure efficient deployment and monitoring of machine learning models in production.
  • Collaborate with cloud architects, security analysts, and other stakeholders to deliver cloud-native security solutions leveraging platforms like AWS, Azure, or GCP.
  • Guide the development of Retrieval-Augmented Generation (RAG) systems, integrating large language models (LLMs) with vector databases to enable real-time, context-aware applications.
  • Establish strong project management frameworks to ensure timely delivery of high-quality solutions that align with organizational objectives.
  • Analyze network traffic, log data, and telemetry to identify and mitigate cybersecurity threats.
  • Ensure data quality, integrity, and compliance with industry standards and regulations such as GDPR, HIPAA, or SOC 2.
  • Foster a culture of innovation by driving the integration of the latest AI/ML techniques into security products and services.
  • Act as a mentor and coach for technical staff, developing talent within the team and promoting a collaborative work environment.
Required Skills and Qualifications:
  • Proficiency in advanced machine learning techniques, including neural networks (e.g., CNNs, Transformers) and anomaly detection.
  • Experience with AI frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  • Strong understanding of MLOps tools and practices (e.g., MLflow, Kubeflow).
  • Demonstrated experience building and deploying RAG systems integrating LLMs and vector databases for real-world applications.
Preferred Qualifications:
  • Hands-on experience in cybersecurity, particularly in incident response or threat hunting.
  • Familiarity with SIEM platforms and cybersecurity compliance standards such as GDPR or SOC 2.
  • Contributions to research, patents, or publications in AI/ML, RAG systems, or cybersecurity.
Education:

BSCS or equivalent required, MSCS or equivalent strongly preferred.

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