JD for Sunnyvale, CA.

Responsibilities:

Design, develop, and implement Retrieval-Augmented Generation (RAG) systems utilizing Large Language Models (LLMs) to enhance our AI capabilities.

Work with vector stores, semantic search, and other relevant technologies to optimize the performance and accuracy of our LLM-based applications.

Conduct experiments to fine-tune LLMs for specific use cases and improve their effectiveness in real-world applications.

Stay up-to-date with the latest advancements in machine learning, natural language processing, and AI technologies to continually improve our methodologies and solutions.

JD for Austin, TX

Machine Learning - Job Description: Screening form will be provided shortly.

6-8 years of experience in machine learning and computer vision, with a proven track record in image processing and analysis.
development and optimization of Computer Vision algorithms and Client models
Continuously refine Client models to improve accuracy
Collaborate with cross-functional teams to integrate AI/Client features into the application.

Skill Requirements:

Proven hands-on experience in Python programming.
Proficient in programming languages relevant to AI/Client development ( TensorFlow, PyTorch).
Strong proficiency in machine learning concepts and practical application.
Familiarity with data analytics packages including pandas, NumPy, and scikit-learn.
Intermediate knowledge in statistics for data analysis and interpretation.
Ability to create insightful and visually appealing graphs using tools like Matplotlib or Seaborn.
Collaborative mindset with excellent communication skills to work effectively in a team.
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Responsibilities:

Develop and maintain Python-based applications, leveraging expertise in Python programming.
Implement and optimize machine learning models for data analysis and insights generation.
Utilize data analytics packages such as pandas, NumPy, and scikit-learn for efficient data manipulation and processing.
Work collaboratively with cross-functional teams to understand business requirements and translate them into technical solutions.
Apply intermediate knowledge in statistics to analyse and interpret complex datasets.
Create visually appealing graphs and visualizations to communicate findings effectively.

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