Machine Learning Engineer – LLM

YO HR Consultancy
  • Post Date: October 6, 2024
  • 15526
  • Applications 0
  • Views 3
Job Overview

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: numpy,pandas,keras,k-nn,algorithms,tensorflow,data science,rnns,random forest,unsupervised learning,decision trees,gans,svm,pytorch,learning,python,supervised learning,reinforcement learning,deep learning,cnns,ml,scikit-learn,transfer learning,large language models (llms),machine learning

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