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Niche LLM plus security specialization reduces applicant density despite metro location and mid-level seniority.
High because required ML/LLM expertise and cybersecurity domain knowledge limit cross-industry transferability.
Explicit 6+ years, required LLM experience and specific ML/LLM and vector DB skills make filters stringent.
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Design and deploy AI systems including Retrieval-Augmented Generation pipelines and LLM applications for security telemetry and unstructured logs analysis.
Develop, fine-tune, and evaluate machine learning models for pattern matching, anomaly detection, and classification on large heterogeneous security datasets.
Build and maintain scalable MLOps pipelines ensuring secure, low-latency production deployment with evaluation and safety alignment frameworks for AI outputs.
6+ years of professional experience deploying ML models in production, including 1+ years with LLMs and generative AI.
Proficiency in Python and ML frameworks such as PyTorch or TensorFlow; experience with LangChain, LlamaIndex, or Hugging Face.
Hands-on experience with vector databases (e.g., Pinecone, Qdrant, Milvus) and semantic search techniques over complex technical data.
Experience with API design, containerization (Docker/Kubernetes), and cloud ML platforms like AWS SageMaker, Azure ML, or GCP Vertex AI.
Strong ability to architect and implement advanced AI/ML solutions in cybersecurity contexts, especially involving large-scale and heterogeneous datasets.
Comfortable leading technical direction and working independently in dynamic, small team environments with ambiguous requirements.
Demonstrated experience integrating AI safety, alignment, and rigorous evaluation in production machine learning systems.