ML Scientist

Responsibilities:

• Develop, train, and deploy state-of-the-art Generative AI and NLP models to enhance AI-driven security solutions.
• Design and optimize AI-Copilot models that assist users in formulating security policies, detecting threats, and responding to risks using natural language.
• Build scalable and production-ready ML pipelines for handling large-scale structured and unstructured security data.
• Work on NLP-based classification, summarization, and anomaly detection algorithms to improve data security insights.
• Develop AI-driven automation and intelligent workflows that help organizations enforce robust security policies.
• Collaborate with Data Engineers, Security Experts, and DevOps teams to integrate. ML models into production environments.

Mindset:

• Entrepreneurial spirit with a "get-things-done" attitude and a commitment to delivering high-quality solutions.
• Comfortable working in an unstructured, fast-paced startup environment.
• Passion for solving complex problems and driving innovation in a high-growth company.

Requirements:

• Generative AI: Experience with LLMs (GPT, Llama, Claude, or similar) and fine-tuning pre-trained models for security use cases.
• AI-Copilot Development: Familiarity with building AI-assistants or AI-driven copilots for enterprise applications.
• NLP and Deep Learning: Strong expertise in transformer models (BERT, T5 Llama, etc. ), text classification, summarization, and Named Entity Recognition (NER).
• ML Frameworks: Proficiency in TensorFlow, PyTorch, Hugging Face, LangChain, or OpenAI APIs.
• Data Processing and Feature Engineering: Experience handling large-scale datasets using Spark, Dask, or Pandas.
• ML Deployment and MLOps: Hands-on experience with MLflow, Docker, Kubernetes, and cloud-based AI services (AWS Sagemaker, Azure AI, GCP Vertex AI).
• Programming Languages: Proficiency in Python and familiarity with SQL for data querying.