Join one of the world's largest professional firms for a hybrid contract to hire opportunity in the Denver, area. Are you skilled in Python coding, experienced in machine learning, and adept at building on Azure?
We are seeking a Data Scientist with a strong background in API connectivity and data management. In this role, you will design and integrate complex systems, leveraging your expertise in Python and machine learning to create innovative solutions on the Azure platform.
A solid understanding of data structures and SQL is essential. Excellent communication skills are crucial, as you will collaborate with diverse teams and stakeholders to ensure seamless integration and functionality.
Contract Duration : 12 months
Required Skills & Experience
- Education : Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
- Experience : At least 5 years of experience in backend development and machine learning.
- Strong proficiency in Python and AI frameworks (TensorFlow, PyTorch).
- Proven experience in developing and implementing generative AI models, with a strong understanding of machine learning algorithms.
- Solid understanding data pre-processing techniques.
- Excellent communication skills
Desired Skills & Experience
Knowledge of cloud computing and AI deployment (Azure, AWS, Google Cloud) is a plus.
What You Will Be Doing
Daily Responsibilities
Integration and Collaboration : Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.
Document and present complex findings and learnings in a clear and concise manner to a non-technical audience.
- Research and Innovation : Stay up-to-date on the latest advancements in generative AI technologies and methodologies such as Retrieval Augmented Generation (RAG), Vector Databases, frameworks such as langchain, etc.
- Algorithm Development : Design and develop algorithms for generative models using deep learning techniques, Natural Language Processing (NLP), Retrieval Augmented Generation (RAG) etc.
- Model Architecture Design : Create and fine-tune generative neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models.
- Data Management : Clean, label, and augment data for training and improving AI models.
- AI Alignment and Testing : Perform rigorous testing and validation of generative AI models to ensure their robustness and reliability.
Address ethical considerations related to generative AI, such as bias, fairness, and transparency.
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