Machine Learning Expert

2 days ago

Islamabad, Islamabad, Pakistan beBee Careers Full time
Job Overview

As a Machine Learning Engineer, you will develop, train, and deploy AI-powered solutions with a focus on Large Language Models (LLMs) and Natural Language Processing (NLP). Your role involves building scalable data pipelines, optimizing ML workflows, and integrating AI models into production environments. Collaborating with cross-functional teams, you'll drive innovation by applying cutting-edge machine learning and deep learning techniques to solve complex challenges.

Key Responsibilities

  1. Develop, train, and deploy machine learning models for predictive and classification tasks, with a focus on LLMs and NLP techniques for AI-powered applications.
  2. Design and implement AI systems and algorithms for solving complex problems.
  3. Build and maintain scalable data pipelines and workflows for data ingestion, transformation, and analysis.
  4. Perform data preprocessing, cleaning, and feature engineering to prepare datasets for analysis.
  5. Conduct exploratory data analysis to identify trends, patterns, and opportunities.
  6. Integrate APIs and external services, including LLMs, to enhance model capabilities and system functionality.
  7. Optimize AI and ML workflows for performance and scalability in production environments.
  8. Monitor model performance, retrain models, and manage the lifecycle of deployed solutions.
  9. Collaborate with cross-functional teams to understand requirements and deliver impactful solutions.

Qualifications

  1. Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
  2. At least 2 years of experience in ML model development and deployment.
  3. Experience working with Large Language Models (LLMs) and NLP techniques for developing AI-powered applications.
  4. Proficiency in Python and popular ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  5. Hands-on experience with data preprocessing, feature engineering, and model optimization.
  6. Strong understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
  7. Experience with SQL and NoSQL databases, as well as cloud platforms (AWS, GCP, Azure).
  8. Ability to integrate ML models into production and optimize workflows for efficiency.
  9. Strong analytical, problem-solving, and collaboration skills.

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