Key Responsibilities:
- Research and Analysis: Conduct research and analysis to understand customer requirements and challenges in implementing Large Language Models (LLMs) for various applications.
- Model Development: Develop and implement machine learning models, including LLMs, tailored to meet customer needs and industry-specific use cases.
- Optimization and Fine-Tuning: Optimize and fine-tune machine learning models for improved performance, accuracy, and efficiency, leveraging techniques such as hyperparameter tuning, transfer learning, and reinforcement learning.
- Deployment and Integration: Deploy machine learning models into production environments, ensuring seamless integration with existing systems and infrastructure.
- Testing and Validation: Conduct rigorous testing and validation of machine learning models to ensure reliability, scalability, and robustness in real-world scenarios.
- Performance Monitoring: Monitor and analyze the performance of deployed models, identifying opportunities for optimization and improvement.
- Collaboration and Communication: Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to gather requirements, iterate on solutions, and communicate progress and findings effectively.
- Documentation and Reporting: Document model development processes, methodologies, and findings, and prepare comprehensive reports and presentations for internal stakeholders and customers.
- Continuous Learning and Innovation: Stay updated on the latest advancements in machine learning and natural language processing (NLP), and explore innovative approaches and techniques to enhance model performance and capabilities.
Desired Skills and experience:
- Bachelors/Masters/Ph.D. degree in Engineering, Machine Learning, Computer Science (or equivalent experience)
- At least 5+ years of relevant experience as an AI Developer
- Should have deep knowledge in machine learning techniques and algorithms, with a focus on natural language processing (NLP) and large language models (LLMs).
- Strong programming skills and proficiency in Python, TensorFlow/PyTorch, Transformers, scikit-learn, NLTK, and spaCy.
- Experience with language modeling evaluation, prompt tuning and engineering, instruction tuning, and/or RLHF
- Prior experience with training and fine-tuning of foundation models such as GPT-3, LLaMA2, AlphaFold, and DALL-E
- Knowledge of deep learning architectures, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models.
- Experience in Information Extraction, Question Answering, Conversational Agents (Chatbots), Data Visualization and/or text-to-image models
- Familiarity with data preprocessing, feature engineering, and model evaluation techniques.
- Experience with new LLM engineering tools like LangChain, Vector DBs, etc.
- Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams.
- Open Source Contributions: Candidates who have contributed to open-source projects related to machine learning, natural language processing, or large language models will be given preference.
