AI Cybersecurity Specialist

2 days ago

Singapore beBeeCybersecurity Full time $120,000 - $180,000
Job Title:

Data Engineer LLM

Cybersecurity AI Engineering RoleAbout the Role:

We are seeking a skilled Data Engineer to join our team and contribute to the development of cutting-edge cybersecurity AI solutions.

Key Responsibilities:
  • Develop and maintain cloud-based data pipelines to ingest, transform, and integrate data from multiple sources for enhanced security analytics.
  • Create interactive dashboards (e.g., Tableau or equivalent) for monitoring and reporting on security insights.
  • Leverage Cloud AI services to enhance AI-driven agent capabilities in security workflows.
  • Design, deploy, and manage vector databases (e.g., Pinecone, PGVector, FAISS) to support AI search and retrieval tasks.
  • Implement agentic workflows using LangChain and open-source tools (e.g., Flowise) to automate complex cyber operations.
  • Implement human-in-the-loop approval processes for critical decision validation.
  • Orchestrate agent-to-agent workflows to enable autonomous collaboration between AI agents.
  • Apply prompt engineering best practices to optimize LLM performance for cybersecurity use cases.
  • Maintain CI/CD pipelines using Git for integration, deployment, and testing of AI solutions.
Requirements:
  • Bachelor's degree or higher in Computer Science, Data Science, Machine Learning, or related field.
  • Hands-on experience in Python, LangChain, NLP, and Computer Vision.
  • Experience with building and deploying AI/LLM-powered applications and agentic workflows.
  • Familiarity with vector database technologies (Pinecone, PGVector, FAISS).
  • CI/CD practices and tools (Git preferred).
  • Cloud platform expertise (AWS, Azure, or GCP) with containerization skills (Docker, Kubernetes).
  • Experience with Cloud AI services (AWS SageMaker/Bedrock or Google Vertex AI).
Technical Skills:
  • Python programming
  • LangChain
  • Natural Language Processing (NLP)
  • Computer Vision
  • Vector databases (Pinecone, PGVector, FAISS)
  • CI/CD tools (Git)
  • Cloud platforms (AWS, Azure, GCP)
  • Containerization (Docker, Kubernetes)
  • Cloud AI services (AWS SageMaker/Bedrock, Google Vertex AI)
  • Data warehousing (Amazon Redshift, Google BigQuery)
  • Data pipeline development
  • Dashboard development (Tableau or equivalent)
  • Open-source automation frameworks (Flowise)

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