Machine Learning Engineer - LLM

at  Yo HR Consultancy

JOB DESCRIPTION/REQUIREMENTS

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