AL/ML lead

We’re looking for a skilled Al/ML lead ( 5+ years) based out of Chennai, for a global computer and network security company.

training and fine-tuning Large Language Models (LLMs)

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.