~20d ago

AI Engineer

Pakistan·Karachimid

Machine Learning EngineerData

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Quick Summary

Overview

Responsibilities Develop and implement machine learning models and algorithms to solve complex business problems and enhance AI-driven applications. Collaborate with cross-functional teams,

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

→Develop and implement machine learning models and algorithms to solve complex business problems and enhance AI-driven applications.
→Collaborate with cross-functional teams, including data scientists and software engineers, to integrate AI solutions into existing products and services.
→Conduct data analysis, preprocessing, and feature engineering to extract valuable insights from large datasets, improving model performance.
→Stay updated on the latest AI and machine learning advancements, research papers, and tools, incorporating them into AI development projects.
→Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks.
→Actively participate in code reviews and collaborative problem-solving sessions, contributing to the improvement of AI solutions and development processes.
→Troubleshoot and resolve AI-related issues, ensuring the reliability and accuracy of AI-driven applications.

Requirements

~1 min read

Bachelor's Degree in Computer Science, Bachelors in Data Science or ML/AI is preferred.
Proven track record as an AI Engineer, with portfolio examples of successful machine learning or AI projects.
Proficiency in programming languages commonly used in AI, such as Python, R, or Java, and experience with ML frameworks like TensorFlow or PyTorch.
Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects.
Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks.
Hands-on experience with Natural Language Processing (NLP), Large Language Models, computer vision, or other AI subfields.
Solid grasp of mathematical concepts relevant to AI, such as linear algebra, calculus, and statistics.
Experience with version control systems like Git, enabling effective collaboration and code management.
Strong analytical and problem-solving skills, crucial for algorithm development and data analysis.
Exceptional communication skills, enabling effective collaboration with both technical teams and business stakeholders to translate AI capabilities into business value.