
AI Model Architect
1 day ago
We are seeking an experienced AI lead with a strong background in machine learning and cybersecurity to join our team at Syncarp.
Job SummaryThis role involves leading the development and implementation of models that understand, process, and generate insights across a wide range of cybersecurity content. You will work closely with a team of ML engineers, cybersecurity SMEs, data engineers, and DevOps to ensure delivery of scalable, performant, and security-aware AI systems.
Key Responsibilities- Fine-tune and domain adapt open-source Large Language Models (LLMs) 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.
- Design and execute 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.
- 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.
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