Artificial Intelligence Engineer

Key Responsibilities:

• Design, develop, and deploy machine learning and deep learning models for real-world applications.
  
• Work with large datasets to extract meaningful insights and features.
  
• Collaborate with data engineers and product teams to define AI/ML use cases.
  
• Apply supervised, unsupervised, and reinforcement learning algorithms depending on the problem.
  
• Build and optimize NLP pipelines, computer vision systems, or recommendation engines.
  
• Implement model validation strategies, hyperparameter tuning, and performance monitoring.
  
• Deploy AI models into production using APIs, containers (Docker), or cloud platforms.
  
• Conduct A/B testing and post-deployment monitoring for model performance.
  
• Stay updated with the latest trends, research, and technologies in AI and machine learning.
  
• Contribute to documentation and knowledge-sharing efforts within the team.
  

Required Skills:

• Proficient in

  Python

  and libraries such as

  NumPy

  ,

  Pandas

  ,

  Scikit-learn

  ,

  Matplotlib

  .
• Strong experience with

  deep learning frameworks

  :

  TensorFlow

  ,

  Keras

  ,

  PyTorch

  .
• Solid understanding of

  machine learning algorithms

  , model training, evaluation, and optimization.
• Experience in one or more AI domains:
  

• NLP

  : Transformers, BERT, GPT, Hugging Face, text classification

• Computer Vision

  : OpenCV, CNNs, YOLO, image classification/object detection

• Recommendation Systems

  or

  Time Series Forecasting

• Strong foundation in

  linear algebra

  ,

  probability

  ,

  statistics

  , and

  optimization

  .
• Exposure to

  model deployment tools

  : Flask/FastAPI, TensorFlow Serving, TorchServe.
• Knowledge of

  MLOps

  practices and tools: MLflow, DVC, Airflow, or Kubeflow.
• Familiarity with

  cloud platforms

  : AWS (SageMaker), GCP (Vertex AI), or Azure ML.
• Experience working in agile environments and using Git for version control.

  Role & responsibilities