Job Description :
Were looking for a skilled Al / ML lead ( 5+ years) based out of Chennai, for a global computer and network security company.
Deep experience in training and fine-tuning Large Language Models (LLMs) such as LLaMA 3 using frameworks like vLLM. The ideal candidate will bring a strong background in machine learning and a practical understanding of the cybersecurity domain - especially around threat intelligence, vulnerabilities, exploits, and configuration analysis.
You will lead the development and implementation of models that understand, process, and generate insights across a wide range of cybersecurity content. You will guide a team of ML engineers and collaborate closely with cybersecurity SMEs, data engineers, and DevOps to ensure delivery of scalable, performant, and security-aware AI systems.
Key Responsibilities :
- 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.
Required Qualifications :
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.(ref : hirist.tech)