Classera - - Amman

Employment: Full Time

Develop and launch comprehensive AI solutions, integrating both traditional machine learning techniques and advanced generative AI technologies.
Create, refine, and implement models utilizing Large Language Models (LLMs) and Generative AI (e.g., OpenAI, GPT, BERT, Hugging Face Transformers) to address real-world business challenges.
Analyze, adapt, and improve data science prototypes, especially in areas like natural language processing (NLP), computer vision, and generative AI.
Design and implement scalable machine learning and deep learning systems, focusing on sophisticated architectures such as transformers, diffusion models, and Retrieval-Augmented Generation (RAG).
Conduct testing, model fine-tuning, and experimentation within AI/ML workflows, using libraries including Hugging Face, TensorFlow, PyTorch, and Langchain.
Research, select, and deploy suitable machine learning and AI tools, including vector databases (e.g., pgvector) and Langgraph for contextual data management in RAG frameworks.
Employ Retrieval-Augmented Generation (RAG) methodologies to merge document retrieval systems with LLMs, facilitating efficient and dynamic responses for real-time applications.
Keep abreast of developments in AI, especially in generative AI, distributed training, responsible AI practices, and toolkits like Langchain, to ensure the integration of cutting-edge technologies in all initiatives.

Requirements

Demonstrated experience as a Machine Learning or AI Engineer, with practical expertise in deploying LLMs, Generative AI models, and RAG frameworks.
In-depth knowledge of data structures, data modeling, software architecture, and large-scale model deployment.
Proficient in Linux environments and cloud platforms (e.g., AWS, GCP, Azure) for AI/ML pipeline development.
Strong programming skills in Python and familiarity with frameworks like TensorFlow, PyTorch, and tools like Hugging Face, Langchain, and Langgraph.
Experienced in deep learning methodologies (e.g., CNNs, RNNs, transformers, generative models) with proficiency in libraries such as Hugging Face, scikit-learn, pandas, and NumPy.
Knowledgeable about RAG architecture, vector databases, and the integration of document retrieval systems with LLMs for advanced AI applications.
Familiar with version control, MLOps, and the deployment of machine learning models in production using technologies such as Docker, Kubernetes, or FastAPI.
Excellent communication abilities, capable of articulating complex AI concepts to both technical and non-technical audiences.
Ability to work independently and collaboratively within cross-functional teams, contributing to shared AI projects.
Strong analytical and problem-solving capabilities, with a track record of managing projects from ideation to execution.
High level of accountability for deliverables, emphasizing innovation and continuous improvement.

4o mini

Post date: 30 October 2024

Publisher: