Part-Time ML Engineer in Lahore — Build Scalable Models

India Abroad

Lahore

On-site

PKR 2,638,664 - 3,569,958

Part time

14 days+

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Job summary

India Abroad in Lahore, Pakistan, is seeking a Machine Learning Engineer to design, develop and deploy ML models on-site. You will collaborate with data scientists and software engineers to integrate ML solutions into client platforms and to build scalable data pipelines for large datasets.

The role requires a strong foundation in statistics, Python/Java/C++, ML libraries, and hands-on experience with model evaluation and tuning.

Qualifications

  • Bachelors or Masters in Computer Science, Engineering or related field.
  • Minimum 2 years of experience in machine learning engineering or related roles.
  • Proficient in Python, Java or C++.
  • Strong background in statistics, mathematics and algorithm design.
  • Experience with ML libraries such as TensorFlow, PyTorch or scikit-learn.
  • Knowledge of SQL or NoSQL databases.

Responsibilities

  • Design, develop and implement machine learning algorithms and models.
  • Collaborate with data scientists and software engineers to integrate ML solutions into platforms.
  • Develop data pipelines to clean, preprocess and transform large datasets.
  • Conduct experiments to evaluate the performance of different algorithms.
  • Continuously monitor and improve the performance of deployed models.
  • Stay updated on the latest ML techniques and tools.

Skills

Python
Java
C++
Statistics
Algorithm design
Communication

Education

Bachelors or Masters in CS/Engineering

Tools

TensorFlow
PyTorch
scikit-learn

Job description

India Abroad in Lahore, Pakistan, is seeking a Machine Learning Engineer to design, develop and deploy ML models on-site. You will collaborate with data scientists and software engineers to integrate ML solutions into client platforms and to build scalable data pipelines for large datasets.

The role requires a strong foundation in statistics, Python/Java/C++, ML libraries, and hands-on experience with model evaluation and tuning.

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