GenAI Engineer – RAG
Job Description
GenAI Engineer – RAG
We are looking for a skilled GenAI Engineer - RAG for our client, an international AI development company based in New York, who will collaborate with a diverse team focused on the development of Retrieval Augmented Generation (RAG)-based LLM solutions that meet business needs.
Who You’ll Work With
You will collaborate with a diverse team focused on the development of Retrieval Augmented Generation-based LLM solutions that meet business needs, requiring a deep understanding of Data Science, Machine Learning, Natural Language Processing, and LLM fundamentals.
Team members will engage in designing and solving specific problems for stakeholders, ensuring effective communication of solutions and insights.
What You’ll Do
Develop, optimize, and deploy RAG-based LLM solutions that meet business needs.
Utilize agent-based prompting techniques such as LangChain.
Implement API-driven development architectures for AI products.
Use vector databases and information retrieval pipelines to extract insights.
Listen to and understand business needs to create targeted solutions for stakeholders.
What You’ll Bring
Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Electrical Engineering, or a closely related quantitative/technical field.
Over 3 years of experience in Python, ML, and AWS Cloud Computing.
Familiarity with AWS Bedrock LLM suite models and prompting strategies.
At least 1 year of experience in NLP and LLMs, focusing on Retrieval Augmented Generation models.
Proficiency in Python for data science and AI development.
Familiarity with agent-based LLM frameworks such as LangChain.
Experience with AI training frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
Knowledge of vector databases and embedding techniques.
Familiarity with DevOps practices, including CI/CD pipelines and containerization tools like Docker and Kubernetes.
Nice to Haves
Use of orchestration tools for AI product development, such as ArgoCD.
Ability to create and explain diagrams for data and solution flows to communicate effectively with team members and internal stakeholders.
Experience in LLM UI/Frontend Design deployment.