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Niche blend of LLM, GPU, and SOC skills reduces applicant pool despite ML domain popularity.
Role requires specialized ML and SOC security expertise, limiting cross-industry transferability.
Multiple mandatory technical skills (RAG, vector DBs, fine-tuning, GPU, Kubernetes) create strict shortlisting filters.
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Design, deploy, and maintain on-premises AI infrastructure supporting Security Operations Center (SOC) workflows, including hosting large language models and managing GPU-accelerated inference servers.
Build and optimize RAG pipelines, vector database solutions, custom fine-tuned AI models, and integrated chatbots/agents to enhance threat intelligence, incident response, and SOC automation.
Containerize and orchestrate AI workloads with Docker, Kubernetes, and Helm; integrate AI tools with SOC platforms (SIEM, EDR, ticketing, threat intelligence).
Bachelor's or Master's degree in Computer Science, AI/ML, Cybersecurity, or equivalent.
Strong experience building RAG pipelines using tools like LangChain, LlamaIndex, Haystack.
Proficient in vector databases such as Chroma, Weaviate, PGVector, Milvus, Qdrant, or FAISS.
Experienced in Python programming with PyTorch, Hugging Face Transformers, GPU acceleration technologies (CUDA, TensorRT, ONNX), Docker, Kubernetes, and on-premises deployment environments.
Has prior experience applying AI technologies specifically in SOC or cybersecurity contexts (threat hunting, log analysis, automated response).
Demonstrates expertise with open-source large language models such as Llama 3, Mistral, Mixtral, Phi, Gemma, or Qwen.
Familiar with MLOps practices including CI/CD pipelines for AI, model monitoring, and advanced multi-agent system frameworks.