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AI Solutions Architect
2 months ago
Position:
AI Solutions Architect
100% remote
Rate / salary is competitive
Overview:
Datamtx LLC is on the lookout for a skilled and knowledgeable AI Solutions Architect to become a vital part of our innovative team, driving advancements in AI technology.
Key Responsibilities:
- AI Solution Development: Create and deploy inventive solutions utilizing large language models (LLMs).
- Refine existing LLMs using proprietary datasets to enhance their performance and adapt them for new use cases.
- Establish and execute training pipelines for LLMs, focusing on efficiency and precision.
Text Generation Model Development: Design, develop, and maintain high-quality text generation models by implementing and assessing various LLM architectures (e.g., Transformers, BERT) tailored for specific tasks.
- Train and fine-tune models on diverse datasets to achieve peak performance.
- Optimize model parameters through iterative experimentation and fine-tuning methodologies.
RAG Model Implementation: Design, develop, and deploy Retrieval-Augmented Generation (RAG) models for producing high-quality text based on user prompts or queries.
- Assess the performance of RAG models using various evaluation metrics.
- Analyze and interpret model outputs to identify improvement opportunities and enhance model performance.
AI-Powered Agent Development: Create and integrate AI-driven agents capable of engaging in conversations with users utilizing the developed RAG models.
- Incorporate natural language understanding (NLU) capabilities to enable agents to comprehend user inputs and respond appropriately.
- Develop and refine dialogue management systems to control agent interactions and ensure coherence.
Data Preparation and Model Fine-tuning: Prepare and curate extensive text datasets for training and validating language models.
- Implement LLM fine-tuning strategies using various optimization algorithms (e.g., Adam, SGD).
- Develop and apply data augmentation techniques to enhance model generalization.
Model Evaluation and Enhancement: Create and implement metrics for assessing the performance of LLMs based on specific project objectives (e.g., accuracy, fluency, coherence).
- Analyze and interpret model outputs to identify areas for enhancement.
Continuous Learning: Stay informed about the latest advancements in LLMs and emerging trends in natural language processing (NLP) and deep learning.
Qualifications:
- Required: Minimum of 5 years of experience in software engineering with a strong emphasis on NLP and machine learning applications.
- Demonstrated expertise in constructing and deploying large language models (LLMs) using frameworks such as TensorFlow, PyTorch, or other relevant tools.
Preferred:
- Experience with various LLM architectures (e.g., BERT, Transformer).
- Strong grasp of natural language processing principles.
- In-depth understanding of advanced RAG techniques.
- Familiarity with NLP libraries (e.g., spaCy, NLTK) and methodologies for fine-tuning and integrating LLMs into diverse applications.
Skills:
- Proficiency in programming language: Python.
Experience with LLM Frameworks:
- Experience with at least one of the following: TensorFlow, PyTorch, Hugging Face Transformers, etc.