Cisco Careers

Deep Knowledge of LLM Architecture: Comprehensive understanding of the architecture underlying large language models, such as Transformer-based models, including GPT (Generative Pre-trained Transformer), and their variants. 
 

Language Model Training and Fine-Tuning: Experience in training large-scale language models from scratch, as well as fine-tuning pre-trained models for specific applications or domains. Experience with agentic frameworks a plus. 
 

Data Preprocessing for NLP: Skills in preprocessing textual data, including tokenization, stemming, lemmatization, and handling of different text encodings. 
 

Transfer Learning and Adaptation: Proficiency in applying transfer learning techniques to adapt existing LLMs to new languages, domains, or specific business needs. 
 

Handling Ambiguity and Context in Text: Ability to design models that effectively handle ambiguities, nuances, and context in natural language processing. 
 

Application of LLMs: Experience in creatively applying LLM technology in diverse areas such as chatbots, content creation, semantic search, and more. 
 

Data Annotation and Evaluation: Skills in designing and implementing data annotation strategies for training LLMs and evaluating their performance using appropriate metrics. 
 

Scalability and Deployment: Experience in scaling LLMs for production environments, ensuring efficiency and robustness in deployment. 

Model Training, Optimization, and Evaluation: This encompasses the complete cycle of training, fine-tuning, and validating language models. You will be designing and adapting LLMs for use in virtual assistants, automated chatbots, content recommendation systems, etc.  

Algorithm Development for Enhanced Language Understanding: Focusing on the development or refinement of algorithms to improve the efficiency and accuracy of language models, especially in natural language understanding and generation tasks. 

Applying LLMs to Cybersecurity: Tailoring language models for cybersecurity purposes, such as analyzing threat intelligence, detecting cyber threats, and automating responses to security incidents. 

Deployment Strategy: Collaborate with software engineering teams to design and implement deployment strategies for machine learning models into security systems, ensuring scalability, reliability, and efficiency. 

Documentation and Best Practices: Establish best practices for machine learning and security operations, and maintain clear documentation of models, data pipelines and metrics 

Experimentation with Emerging Technologies and Methods: Actively exploring new technologies and methodologies in language model development, including experimental frameworks, software tools, and cutting-edge approaches. 

Mentoring and Cross-Functional Collaboration: Providing mentorship to team members and working collaboratively with cross-functional teams to ensure cohesive development and implementation of language model projects. 

BA / BS degree with 7+ years of experience (or) MS degree with 5+ years of experience as a machine learning engineer 

Solid experience in machine learning engineering, with a strong portfolio of successful projects 

Extensive experience in building machine learning systems and scalable solutions 

Expertise in machine learning algorithms, deep learning, and statistical modeling 
 

Advanced degree in Computer Science, Data Science, Statistics, Computational Linguistics or a related field. 

Proficiency in programming languages such as Python or R, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn) 

Excellent problem-solving and communication skills, with the ability to explain complex concepts to non-technical stakeholders 

Proven ability to work collaboratively in cross-functional teams