AI/ML Engineer: Time-Series Data Specialist

2 days ago


Islamabad, Islamabad, Pakistan beBee Careers Full time

Job Summary:

We are seeking a highly skilled AI Engineer with expertise in Deep Learning (DL) and a strong understanding of Deep Reinforcement Learning (DRL).

The ideal candidate will have hands-on experience in designing, training, and deploying deep learning models for time-series data applications.

  • Main Responsibilities:
  • Design and develop deep learning models for time-series data applications
  • Implement, train, and fine-tune deep reinforcement learning algorithms (e.g., PPO, DDPG, SAC) for control and optimization tasks
  • Develop scalable APIs for deploying AI models and integrating them with cloud-based infrastructures
  • Work with AWS cloud services for training, deployment, and monitoring of AI models
  • Optimize model inference performance and handle large-scale data processing pipelines
  • Ensure robustness, reliability, and efficiency of AI models through continuous monitoring and retraining strategies

Requirements:

  • Education: Bachelor's degree in Computer Science, Engineering, or a related field
  • Experience: 2+ years of hands-on experience as an AI engineer or related engineering role

Key Skills:

  • Deep Learning Expertise: Extensive experience with LSTMs and Transformers for solving time-series data problems
  • Reinforcement Learning Knowledge: Hands-on experience with DRL frameworks like Stable-Baselines3, Gymnasium, or custom implementations
  • Programming: Strong proficiency in Python, PyTorch, TensorFlow, and deep learning libraries
  • Model APIs: Experience in developing and deploying model inference APIs using FastAPI
  • Problem-Solving Skills: Ability to work with large-scale, real-world time-series data, optimize deep learning models, and implement innovative AI-driven solutions

Working Conditions:

  • Location: Onsite - Multi Gardens B-17, Islamabad
  • Experience: Minimum 3 years
  • Work Timings / Day: Monday to Friday (9:00 AM - 6:00 PM)
  • Work Mode: Onsite


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