AI Engineer - LLM Development Project

AI Engineer

We are seeking a skilled AI Engineer with a strong background in Large Language Models (LLMs), a deep understanding of AI concepts, and hands-on experience with the LLM framework. As a member of our team, you will play a pivotal role in designing, developing, and deploying agent-based AI solutions. This role requires leveraging cutting-edge tools and methodologies to enhance performance, accuracy, and scalability, with a particular focus on AI deployment and scaling best practices.

Master's or Ph.D. in Computer Science or a related field (Ph.D. preferred). The ideal candidate has strong experience in deep learning (PyTorch/Tensor Flow), computer vision (CNNs, Vision Transformers) and/or large language models (GPT, Deep Seek, etc.). Proficiency in Python, ML pipelines, and cloud platforms is essential. Familiarity with multi-agent systems or reinforcement learning is a plus. Experience deploying models using AWS, CDK, and Docker is required.

Key Responsibilities

• Agent-Based Application Development:
  Develop intelligent, autonomous agents capable of handling complex tasks within AI applications to drive performance and user satisfaction.
• Prompt Engineering:
  Design and optimize prompt structures to improve the accuracy and relevance of AI outputs, ensuring robust interactions with LLMs.
• LLM Framework Implementation:
  Implement and fine-tune large language models, utilizing frameworks for optimal response generation, efficiency, and scalability.
• AI Deployment & Scaling:
  Oversee the deployment and scaling of LLMs in production environments, ensuring effective resource use and high performance under varying workloads.

Requirements

• LLM Framework Expertise:
  Proven experience with large language model frameworks, including deployment, fine-tuning, and inference techniques.
• Machine Learning Background:
  Strong foundation in machine learning principles, particularly as applied to LLMs and agent-based architectures.
• Programming
  
  Skills:
  
  Proficiency in Python and familiarity with essential AI libraries (e.g., PyTorch, Langchain/Langgraph, Bedrock).
• Deployment and Scalability:
  Familiarity with deploying and scaling AI solutions in production environments to ensure reliability and efficiency.