Machine Learning Engineer in GenAI

Compunnel Software Group - Plano, TX

Full-time - Mid Level

Plano, TX

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer (MLE) position focuses on developing and implementing machine learning models, particularly in the context of Generative AI (GenAI). The ideal candidate will have a strong foundation in Python and be proficient with data science libraries such as PySpark and Databricks. The role requires expertise in utilizing various Python packages, including Hugging Face, Langchain, and scikit-learn, to create systems similar to ChatGPT. Candidates will be expected to have hands-on experience with a range of algorithms, including Random Forest, Decision Trees, K-NN, Linear and Logistic Regression, K-means Clustering, Adaboost, and SVM. A significant aspect of the role involves designing and developing GenAI pipelines using Retrieval-Augmented Generation (RAG) techniques. While experience with Snowflake is not critical, familiarity with NoSQL databases is essential for this position. This role is particularly suited for individuals who are passionate about machine learning and have a keen interest in the evolving field of Generative AI. The successful candidate will work collaboratively with cross-functional teams to deliver innovative solutions that leverage machine learning technologies.

Responsibilities

• Develop and implement machine learning models for Generative AI applications.
• Utilize Python and data science libraries such as PySpark and Databricks.
• Work with Python packages like Hugging Face, Langchain, and scikit-learn to create ChatGPT-like systems.
• Design and develop GenAI pipelines using Retrieval-Augmented Generation (RAG).
• Apply various machine learning algorithms including Random Forest, Decision Trees, K-NN, Linear and Logistic Regression, K-means Clustering, Adaboost, and SVM.
• Collaborate with cross-functional teams to deliver machine learning solutions.
• Ensure the integration of NoSQL databases in machine learning workflows.

Requirements

• Proficiency in Python and data science libraries such as PySpark and Databricks.
• Experience with machine learning frameworks and libraries including Hugging Face, Langchain, and scikit-learn.
• Strong understanding of machine learning algorithms such as Random Forest, Decision Trees, K-NN, Linear and Logistic Regression, K-means Clustering, Adaboost, and SVM.
• Experience in designing and developing GenAI pipelines using RAG.
• Familiarity with NoSQL databases.

Nice-to-haves

• Experience with Snowflake is a plus but not critical.