AI Engineer: ML & MLOps Pro

abacus-consulting-3; Lahore; On-site; PKR 1,800,000 - 3,200,000; Full time

Job summary

abacus-consulting-3 in Lahore is seeking a skilled AI Engineer to design, deploy, and maintain AI and machine learning solutions addressing complex business challenges.

The role requires hands-on experience with Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), data preprocessing, model evaluation, and cloud platforms. You will collaborate with cross-functional teams and ensure robust ML deployment and MLOps practices.

Qualifications

• 2-4 years of hands-on experience in AI or Machine Learning.
• Strong programming skills in Python.
• Experience with TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of data preprocessing, feature engineering and model evaluation.
• Familiarity with SQL or NoSQL databases.
• Strong analytical and problem-solving skills.
• Bachelor's/Masters degree in AI, Data Science, or a related field.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of model deployment and MLOps concepts.

Responsibilities

• Design, develop, and deploy AI and machine learning models
• Analyze and process large datasets for model training and optimization
• Build and implement solutions using machine learning, NLP, or computer vision
• Collaborate with cross-functional teams including engineering and product
• Monitor, evaluate, and improve model performance
• Integrate AI solutions into existing systems
• Maintain clear documentation of models and processes

Skills

Python

Data analysis

ML model evaluation

Education

Bachelor's/Master's in AI or Data Science

Tools

TensorFlow

PyTorch

Scikit-learn

Job description

abacus-consulting-3 in Lahore is seeking a skilled AI Engineer to design, deploy, and maintain AI and machine learning solutions addressing complex business challenges.

The role requires hands-on experience with Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), data preprocessing, model evaluation, and cloud platforms. You will collaborate with cross-functional teams and ensure robust ML deployment and MLOps practices.