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Mid-level (6+ years), metro location and known cybersecurity brand yield moderate applicant competition.
Core ML skills transferable but cybersecurity domain expertise increases industry specificity.
Explicit 6+ years, 1+ LLM experience, and many mandatory tech stacks create high filtering strictness.
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Design and deploy AI systems including Retrieval-Augmented Generation (RAG) pipelines and LLM applications for security telemetry and logs.
Develop, fine-tune, and evaluate ML/DL models for pattern matching, anomaly detection, and classification on large security datasets.
Build and maintain scalable MLOps pipelines ensuring secure, low-latency production model deployments with safety and trust guardrails.
6+ years professional experience in deploying machine learning models into production, including 1+ year with LLMs and generative AI.
Proficiency in Python and AI/NLP frameworks such as PyTorch or TensorFlow, and tools like LangChain, LlamaIndex, or Hugging Face.
Hands-on experience with vector databases (e.g., Pinecone, Qdrant, Milvus) and semantic search techniques.
Strong knowledge of API design, containerization (Docker/Kubernetes), and cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
Experienced in cybersecurity domain or working with complex security datasets and telemetry.
Capable of setting technical direction and handling ambiguity in small, fast-paced teams.
Skilled in architecting and operationalizing AI systems focused on security use cases and trust/alignment frameworks.