Artificial Intelligence Engineer

Title - AI Engineer

Type - Contract

Location - Remote

Roles and Responsibilities:

• Design, develop, and deploy AI models, particularly focusing on Large Language Models (LLM) to solve complex problems.
• Implement Retrieval-Augmented Generation (RAG) frameworks to enhance information retrieval and response accuracy in AI systems.
• Develop and maintain AI workflows using Langchain to integrate various data sources and AI components effectively.
• Write clean, efficient, and scalable code primarily in Python to build AI applications and prototypes.
• Collaborate with cross-functional teams including data scientists, engineers, and product managers to translate business requirements into AI solutions.
• Continuously research and adopt new AI technologies and methodologies to keep the organization at the forefront of innovation.
• Optimize AI models for performance, accuracy, and resource efficiency.
• Monitor and troubleshoot AI systems in production to ensure reliability and robustness.

Qualifications:

• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or a related field.
• 2 to 5 years of hands-on experience in AI engineering or related roles.
• Proven experience working with Large Language Models (LLM) and implementing RAG strategies.
• Strong proficiency in Python programming and AI/ML libraries.
• Familiarity with Lang chain or similar frameworks for building AI pipelines.
• Solid understanding of machine learning concepts, natural language processing, and data structures.
• Excellent problem-solving skills and the ability to work independently as well as in a team environment.
• Strong communication skills to articulate complex AI concepts to non-technical stakeholders.

Tools and Technologies:

• Python (primary programming language)
• Large Language Models (LLM) such as GPT, BERT, or similar
• Retrieval-Augmented Generation (RAG) frameworks and methodologies
• Langchain for AI workflow orchestration
• Machine learning libraries and frameworks like TensorFlow, PyTorch, or Hugging Face Transformers
• Version control tools such as Git
• Cloud platforms (AWS, Azure, or GCP) for deploying AI models (preferred)