Match score not available

Machine Learning Engineer - LLM

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Indonesia

Offer summary

Qualifications:

Bachelor’s/Master’s in related field, 2+ years Python experience, 1+ year ML framework experience, Proficient in TensorFlow/PyTorch, Strong knowledge of Scikit-learn.

Key responsabilities:

Develop and deploy machine learning models
Implement preprocessing pipelines with Scikit-learn
Apply supervised and unsupervised learning algorithms
Design deep learning architectures like CNNs and RNNs
Explore reinforcement learning techniques

Yo Hr Consultancy SME https://www.yohrconsultancy.com/

11 - 50 Employees

See more Yo Hr Consultancy offers

Job description

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.