Lead AI Scientist/Engineer

As a Sr. AI/ML Engineer at our company, you will have the exciting opportunity to work on cutting-edge artificial intelligence projects. Your main responsibilities will include: - Model Development & Fine-Tuning: You will assist in training, fine-tuning, and evaluating generative models like LLMs on specific datasets. - Agentic AI: Design and develop intelligent AI agents for vulnerability and endpoint management, capable of integrating with databases and servers for data collection, analysis, and actionable recommendations. - Prompt Engineering: Your role will involve designing, iterating, and refining prompts to steer model behavior effectively for applications like chatbots, summarization, and content creation. - RAG Systems: You will contribute to building and maintaining Retrieval-Augmented Generation (RAG) pipelines, grounding models in factual, up-to-date information from external data sources. - Data Management: Collect, clean, and preprocess large volumes of structured and unstructured data for model training and analysis. - Collaboration & Integration: Work closely with software engineers to integrate GenAI models into user-facing applications via APIs. - Research & Experimentation: Stay updated with the latest research in GenAI, conduct experiments to explore new tools, techniques, and model architectures. In terms of qualifications, we are looking for candidates with the following requirements: - Educational Background: Bachelors or Masters degree in Computer Science, Data Science, AI, Statistics, or related quantitative field. - Programming Skills: Strong proficiency in Python and familiarity with core data science libraries like Pandas, NumPy, and Scikit-learn. - ML/DL Fundamentals: Solid understanding of machine learning concepts, especially neural networks and the Transformer architecture. - NLP Foundations: Basic knowledge of NLP concepts including text preprocessing, word embeddings, and sequence modeling. - Problem-Solving Mindset: Curious and analytical approach to solving complex problems. Preferred qualifications include hands-on experience with LLMs, familiarity with deep learning frameworks like PyTorch or TensorFlow, exposure to libraries like Hugging Face Transformers, and basic understanding of cloud environments such as AWS, GCP, or Azure. Experience using Git for team-oriented code development is also beneficial. As a Sr. AI/ML Engineer at our company, you will have the exciting opportunity to work on cutting-edge artificial intelligence projects. Your main responsibilities will include: - Model Development & Fine-Tuning: You will assist in training, fine-tuning, and evaluating generative models like LLMs on specific datasets. - Agentic AI: Design and develop intelligent AI agents for vulnerability and endpoint management, capable of integrating with databases and servers for data collection, analysis, and actionable recommendations. - Prompt Engineering: Your role will involve designing, iterating, and refining prompts to steer model behavior effectively for applications like chatbots, summarization, and content creation. - RAG Systems: You will contribute to building and maintaining Retrieval-Augmented Generation (RAG) pipelines, grounding models in factual, up-to-date information from external data sources. - Data Management: Collect, clean, and preprocess large volumes of structured and unstructured data for model training and analysis. - Collaboration & Integration: Work closely with software engineers to integrate GenAI models into user-facing applications via APIs. - Research & Experimentation: Stay updated with the latest research in GenAI, conduct experiments to explore new tools, techniques, and model architectures. In terms of qualifications, we are looking for candidates with the following requirements: - Educational Background: Bachelors or Masters degree in Computer Science, Data Science, AI, Statistics, or related quantitative field. - Programming Skills: Strong proficiency in Python and familiarity with core data science libraries like Pandas, NumPy, and Scikit-learn. - ML/DL Fundamentals: Solid understanding of machine learning concepts, especially neural networks and the Transformer architecture. - NLP Foundations: Basic knowledge of NLP concepts including text preprocessing, word embeddings, and sequence modeling. - Problem-Solving Mindset: Curious and analytical approach to solving complex problems. Preferred qualifications include hands-on experience with LLMs, familiarity with deep learning frameworks like PyTorch or TensorFlow, exposure to libraries like Hugging Face Transformers, and basic understanding of cloud environments such as AWS, GCP, or Azure. Experience using Git for team-oriented code development is also beneficial.