LLM - Python for Machine Learning
Experience: 2 - 15 Years
Location- Permanent Remote Anywhere in the world
Contract Duration: 6 Months
Opportunity- Full Time, 8 hours, 5 hours Mandatory overlap with PST
Total Years Of exp- 2+ years Mandatory
Mandatory Skills- Python: min 2 years, Python for Data Science: min 1 yr, Machine Learning: min 1 yr, PyTorch: min 1 yr, Keras: min 1 yr, Tensorflow: min 1 yr
Key skills: Python, ML, PyTorch, Keras, TensorFlow
Job Responsibilities
- Develop, train, and deploy machine learning models using TensorFlow and/or PyTorch.
- Implement machine learning models and preprocessing pipelines using Scikit-learn.
- Apply supervised and unsupervised learning algorithms, including SVM, Decision Trees, Random Forest, and k-NN, to solve complex problems.
- Design and implement deep learning architectures such as CNNs, RNNs, GANs, and transfer learning models.
- Explore and apply reinforcement learning techniques to enhance AI solutions.
Job Requirements
- Bachelor’s/Master’s degree in Engineering, Computer Science, or a related field.
- At least 2+ years of experience as a Python-focused Engineer.
- 1+ years of experience with Python-based frameworks for machine learning.
- Proficiency in TensorFlow and/or PyTorch for model development and deployment.
- Strong knowledge of Scikit-learn for machine learning model implementation.
- In-depth understanding of supervised and unsupervised learning algorithms, as well as deep learning architectures.
Nice To Have
- Experience with Keras for quick prototyping of deep learning models.
- Proficiency in Pandas and Numpy for data manipulation and preprocessing.
- Familiarity with Large Language Models (LLMs) and their applications in AI.
- Excellent spoken and written English communication skills.
Skills: large language models (llms),pandas,data science,algorithms,k-nn,scikit-learn,pytorch,tensorflow,rnns,python,supervised learning,svm,machine learning,gans,unsupervised learning,deep learning,learning,reinforcement learning,numpy,ai,ml,cnns,transfer learning,random forest,decision trees,keras
