Artificial Intelligence AI Engineer

Were on the lookout for a talented and innovative AI Engineer to join our dynamic team. If you have a solid background in artificial intelligence machine learning and software engineering we want to hear from you. As an AI Engineer your primary responsibility will be to develop cuttingedge AI solutions that tackle complex challenges and enhance our products and services. This role requires close collaboration with crossfunctional teams to design implement and deploy AI models and algorithms driving business growth and improving customer experiences.

Responsibilities:

• AI Solution Development: Develop and implement AI models and algorithms utilizing techniques such as supervised learning unsupervised learning and deep learning to address business challenges effectively.
  
• Data Preparation and Preprocessing: Clean preprocess and transform large datasets to extract meaningful insights and features for training AI models while ensuring data quality and integrity throughout the data pipeline.
  
• Model Training and Evaluation: Build train and evaluate machine learning models using appropriate libraries and frameworks finetuning model parameters and hyperparameters for optimal performance and accuracy.
  
• Deployment and Integration: Deploy AI models into production environments integrating them seamlessly with existing systems and infrastructure ensuring scalability reliability and performance in collaboration with DevOps and IT teams.
  
• Continuous Improvement: Stay updated with the latest advancements in artificial intelligence and machine learning research tools and techniques identifying opportunities for innovation and improvement within the AI ecosystem.
  
• Collaboration and Communication: Work closely with crossfunctional teams including data scientists software engineers and business stakeholders to understand requirements gather feedback and iterate on AI solutions effectively.
  
• Documentation and Reporting: Document code methodologies and findings comprehensively generating reports and presentations to communicate insights clearly.
  

Preferred Programming Languages / Tools:

• Python: Widely used for AI development due to its simplicity extensive ecosystem of libraries (e.g. NumPy TensorFlow PyTorch) and strong community support.
  
• R: Particularly useful for statistical analysis data visualization and machine learning model development.
  

Integrated Development Environments (IDEs):

• Jupyter Notebooks: Interactive environment ideal for data exploration prototyping and code sharing.
  
• PyCharm: Powerful IDE for Python development with features like code completion debugging and version control integration.
  
• RStudio: Integrated development environment tailored for R programming focusing on data science and statistical analysis.
  

Deep Learning Frameworks:

• TensorFlow: Opensource framework by Google for building and training deep learning models.
  
• PyTorch: Opensource framework by Facebooks AI Research lab known for flexibility and dynamic computation graph.
  
• Keras: Highlevel neural networks API compatible with TensorFlow and other backends.
  

Machine Learning Libraries:

• scikitlearn: Simple and efficient tools for data mining and analysis.
  
• XGBoost: Scalable implementation of gradient boosting algorithms widely used for structured data.
  
• LightGBM: Gradient boosting framework with high efficiency and support for large datasets.
  

Data Manipulation and Analysis:

• NumPy: Fundamental package for scientific computing in Python.
  
• Pandas: Data manipulation library offering data structures and operations for structured data.
  
• Matplotlib: Comprehensive library for creating visualizations in Python.
  

Data Visualization:

• Seaborn: Statistical data visualization library based on Matplotlib.
  
• Plotly: Interactive visualization library for creating rich plots and dashboards.
  
• Bokeh: Python library for creating interactive visualizations in web browsers.
  

Development Environments:

• Jupyter Notebooks PyCharm Visual Studio Code: Integrated environments for code editing debugging and version control.
  

Deployment and Serving:

• TensorFlow Serving: Highperformance system for deploying TensorFlow models.
  
• Docker: Platform for packaging and running applications within containers.
  
• Kubernetes: Container orchestration platform for automating deployment and management.
  

Version Control:

• Git: Distributed version control system for tracking changes in source code during development.
  

Cloud Platforms:

• Amazon Web Services (AWS) Microsoft Azure Google Cloud Platform (GCP): Cloud computing platforms offering AI services and scalable infrastructure.
  

Other Tools and Libraries:

• OpenCV: Library for computer vision tasks such as image processing and object detection.
  
• NLTK (Natural Language Toolkit): Library for natural language processing tasks.
  
• Spark MLlib: Machine learning library built on Apache Spark for distributed data processing.
  

• it is a work from home position for candidates based in Pakistan.
  

• Mon Fri 8 PM 5 AM Pakistan time