MindSource
Machine Learning Engineer-12 months Contract -REMOTE
MindSource, San Mateo, California, 94409
Title: Machine Learning Engineer Location: REMOTE Duration: 12 months Contract Machine Learning Engineer About the Role We are looking for a skilled Machine Learning Engineer to join our team This role focuses on scaling machine learning systems and building innovative applications using Generative AI. As part of a collaborative, cross-functional team, you will have the opportunity to develop user-facing projects, such as AI-powered quizzes, that bring advanced machine learning to life. Your contributions will be pivotal in enhancing our AI capabilities and creating engaging, impactful solutions. What You'll Be Doing Building, deploying, and scaling machine learning models and applications, especially in the context of Generative AI Developing end-to-end applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and other NLP techniques Working with Python, Databricks, Langchain, PySpark, Airflow, Jenkins, and AWS to drive development Collaborating closely with analytics and data teams and business stakeholders to align on requirements and objectives Contributing both independently and in team settings, providing regular updates to stakeholders and delivering robust AI solutions What We're Looking For Strong software engineering skills, particularly in Python Experience with frameworks and tools relevant to large-scale machine learning, including Databricks, PySpark, and Airflow Familiarity with LLMs, RAG, Lang chain, and developing AI applications Proficiency in AWS and experience deploying applications on cloud platforms Solid understanding of database schema design and querying Bachelor's degree in Computer Science Nice to Have Experience building end-to-end applications with LLMs and user-facing projects Knowledge of NLP techniques and experience with Generative AI Strong problem-solving abilities, excellent communication skills, and the ability to work effectively with minimal guidance in a fully remote environment