Machine Learning (ML)
Python
ChatGPT
Deep LearningWhat you will be doing at Webkul?
- Python Proficiency and API Integration:
- Demonstrate strong proficiency in Python programming language.
- Design and implement scalable, efficient, and maintainable code for machine learning applications.
- Integrate machine learning models with APIs to facilitate seamless communication between different software components.
- Machine Learning Model Deployment, Training, and Performance:
- Develop and deploy machine learning models for real-world applications.
- Conduct model training, optimization, and performance evaluation.
- Collaborate with cross-functional teams to ensure the successful integration of machine learning solutions into production systems.
- Large Language Model Understanding and Integration:
- Possess a deep understanding of large language models (LLMs) and their applications.
- Integrate LLMs into existing systems and workflows to enhance natural language processing capabilities.
- Stay abreast of the latest advancements in large language models and contribute insights to the team.
- Langchain and RAG-Based Systems (e.g., LLamaindex):
- Familiarity with Langchain and RAG-based systems, such as LLamaindex, will be a significant advantage.
- Work on the design and implementation of systems that leverage Langchain and RAG-based approaches for enhanced performance and functionality.
- LLM Integration with Vector Databases (e.g., Pinecone):
- Experience in integrating large language models with vector databases, such as Pinecone, for efficient storage and retrieval of information.
- Optimize the integration of LLMs with vector databases to ensure high-performance and low-latency interactions.
- Natural Language Processing (NLP):
- Expertise in NLP techniques such as tokenization, named entity recognition, sentiment analysis, and language translation.
- Experience with NLP libraries and frameworks like NLTK, SpaCy, Hugging Face Transformers
- Computer Vision:
- Proficiency in computer vision tasks such as image classification, object detection, segmentation, and image generation.
- Experience with computer vision libraries like OpenCV, PIL, and frameworks like TensorFlow, PyTorch, and Keras.
- Deep Learning:
- Strong understanding of deep learning concepts and architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
- Proficiency in using deep learning frameworks like TensorFlow, PyTorch, and Keras.
- Experience with model optimization, hyperparameter tuning, and transfer learning.
- Data Manipulation:
- Strong skills in data manipulation and analysis using libraries like Pandas, NumPy, and SciPy.
- Proficiency in data cleaning, preprocessing, and augmentation techniques.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Full Stack Developer - Averlon