LLM Developer

We are seeking a talented and motivated Machine Learning Developer to join our dynamic team. The ideal candidate will have a strong background in data preprocessing, large language models (LLMs), retrieval-augmented generation (RAG), and experience working with both open-source and third-party APIs for LLMs. Additionally, the candidate should be proficient in using AWS services such as EC2, Lambda, Glue, and S3 to manage and optimize data pipelines. The role involves developing, implementing, and optimizing machine learning models and data pipelines to support our innovative AI-driven solutions.

Responsibilities:
Develop and maintain data preprocessing pipelines using Pandas and other relevant
tools.
Design and implement machine learning models, with a focus on large language models
(LLMs) and retrieval-augmented generation (RAG) techniques.
Integrate open-source and third-party APIs for LLMs to enhance our AI capabilities.
Utilize AWS services (EC2, Lambda, Glue, S3) to manage and optimize data pipelines
and machine learning workflows.
Collaborate with cross-functional teams to understand project requirements and deliver
effective machine learning solutions.
Perform data analysis and feature engineering to prepare datasets for training and
evaluation.
Optimize machine learning models for performance and scalability.
Stay updated with the latest advancements in machine learning, LLMs, and related
technologies.
Document the development process, methodologies, and model performance.
Participate in code reviews, testing, and debugging to ensure high-quality code.

Requirements:
Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a
related field.
Proven experience in developing and maintaining data preprocessing pipelines using
learn, and TensorFlow/PyTorch.
Experience with version control systems (e.g., Git) and collaborative development
workflows.
Excellent problem-solving skills and attention to detail.
Strong communication skills and the ability to work effectively in a team environment.
Preferred Qualifications:
Experience with additional cloud platforms (e.g., Google Cloud, Azure) for deploying
machine learning models.
Knowledge of natural language processing (NLP) techniques and libraries.
Familiarity with MLOps practices and tools for continuous integration and deployment.
Experience with data visualization tools and libraries.