AI/ML Lead

Key Responsibility

• Lead the fine-tuning and domain adaptation of open-source LLMs (e.g., LLaMA 3) using frameworks like vLLM, HuggingFace, DeepSpeed, and PEFT techniques.
• Develop data pipelines to ingest, clean, and structure cybersecurity data, including threat intelligence reports, CVEs, exploits, malware analysis, and configuration files.
• Collaborate with cybersecurity analysts to build taxonomy and structured knowledge representations to embed into LLMs.
• Drive the design and execution of evaluation frameworks specific to cybersecurity tasks (e.g., classification, summarization, anomaly detection).
• Own the lifecycle of model development including training, inference optimization, testing, and deployment.
• Provide technical leadership and mentorship to a team of ML engineers and researchers.
• Stay current with advances in LLM architectures, cybersecurity datasets, and AI-based threat detection.
• Advocate for ethical AI use and model robustness, especially given the sensitive nature of cybersecurity data

Requirements Required Skills:

• 5+ years of experience in machine learning, with at least 2 years focused on LLM training or fine-tuning.
• Strong experience with vLLM, HuggingFace Transformers, LoRA/QLoRA, and distributed training techniques.
• Proven experience working with cybersecurity data—ideally including MITRE ATT&CK, CVE/NVD databases, YARA rules, Snort/Suricata rules, STIX/TAXII, or malware datasets.
• Proficiency in Python, ML libraries (PyTorch, Transformers), and MLOps practices.
• Familiarity with prompt engineering, RAG (Retrieval-Augmented Generation), and vector stores like FAISS or Weaviate.
• Demonstrated ability to lead projects and collaborate across interdisciplinary teams.
• Excellent problem-solving skills and strong written & verbal communication.

Nice to Have

• Experience deploying models via vLLM in production environments with FastAPI or similar APIs.
• Knowledge of cloud-based ML training (AWS/GCP/Azure) and GPU infrastructure.
• Background in reverse engineering, malware analysis, red teaming, or threat hunting.
• Publications, open-source contributions, or technical blogs in the intersection of AI and cybersecurity.